emerging economy resilience and vulnerability to adverse

Emerging economy resilience and vulnerability to adverse exogenous

economic shocks: The case of sub-Saharan Africa

Xolile Msutwana

10669303

A research project submitted to the Gordon Institute of Business Science,

University of Pretoria, in partial fulfilment of the requirements for the degree of

Master of Business Administration.

11 November 2013

© 2014 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria.

i

Abstract

The impact of the recent global financial crisis on the global economy has highlighted the

level of integration of economies and the potential spillover effects as a result thereof. The

implications are that the negative effects of the crisis can quickly spread to other

economies through numerous transmission mechanisms. The response of developing or

emerging economies to these unpredictable exogenous shocks becomes a topical issue.

The concepts of economic vulnerability to and resilience against adverse exogenous

shocks for emerging economies have since taken centre stage in many economic forums.

Policy makers for emerging economies have come to the realisation that the increased

economic vulnerability and a lack of economic resilience in their economies can erode the

hard-fought-for gains in economic growth over the past decade and potentially harm their

prospects as attractive destinations for foreign direct investment (FDI).

This research analysed the resilience and vulnerability of emerging economies against

adverse shocks using the sub-Saharan African (SSA) region as a case. The research used

previous literature on emerging economies’ vulnerability and resilience to formulate four

hypotheses around the major overarching themes of vulnerability and resilience. Two

hypotheses looked at two functions of vulnerability, i.e. trade openness and financial

integration, and two functions of resilience, i.e. international reserves accumulation and

economic concentration.

The findings of this research study were that SSA economies were vulnerable and not

resilient against adverse exogenous shocks, and that few economies in the SSA region

were prepared to successfully manoeuvre in an economic crisis. The structure of these

economies inherently rendered these economies vulnerable. However, these economic

structures also allowed the SSA region to achieve the high economic growth experienced

during the past decade. The output of the methodology utilised in this research study

resulted in a model that can be used to reduce the likelihood of an SSA economy being

severely affected by an adverse economic shock.

Keywords: vulnerability, resilience, emerging economies, foreign direct investment, sub-

Saharan Africa


ii

Declaration

I declare that this research project is my own work. It is submitted in partial fulfilment of the

requirements for the degree of Master of Business Administration at the Gordon Institute of

Business Science, University of Pretoria. It has not been submitted before for any degree

or examination in any other University. I further declare that I have obtained the necessary

authorisation and consent to carry out this research.

Xolile Msutwana

11/11/2013

Date


iii

Acknowledgements

I would like to thank the following people for their support and encouragement throughout

the research process and the MBA journey.

First and foremost, I would like to send my heartfelt thank you to Zanele – my best friend,

confidante, biggest cheerleader and future wife. At times, you felt as though you were

doing the MBA with me which just goes to show how involved you were throughout my

entire MBA journey, I look forward to spending more time (yes, more Saturdays as well)

with you.

To my mother, Nomaxaba Msutwana, you have always been my pillar of strength and your

constant encouragement kept me going. I could not have asked for a better parent and

your ability to guide me to the right choices still amazes me. Thank you for instilling in me

the perseverance and ambition to succeed.

To Lind Sing, my supervisor, thank you for your patience in helping me to develop this idea

from the first meeting and for your constructive and acute feedback throughout the

research process. It has been an absolute pleasure working with you.

Jeannie van den Heever, my editor, thank you for your professional assistance in putting

together the research report. Your contribution has been invaluable.

To my brother, Mxolisi, with whom I embarked on this MBA journey, it has been an

unbelievable experience and a culmination of an extraordinary journey together. And to my

sister, Thando, thank you for your constant encouragement.

Finally, to my family (and Zukiswa) and friends, thank you for your understanding and I

look forward to spending more time with you.

Xolile


iv

Table of Contents

Abstract ........................................................................................................................... i

Declaration ..................................................................................................................... ii

Acknowledgements ....................................................................................................... iii

CHAPTER 1 Introduction to research problem ........................................................... 1

1.1 Introduction ............................................................................................................... 3

1.2 Purpose of the research ........................................................................................... 3

1.3 Problem definition ..................................................................................................... 4

1.3.1 Impact on foreign direct investment ........................................................................... 5

1.3.2 Emerging economy vulnerability and resilience ......................................................... 6

1.4 Context ..................................................................................................................... 7

1.4.1 Resilient dynamism .................................................................................................... 7

1.4.2 Exploring economic resilience.................................................................................... 8

1.5 Conclusion ................................................................................................................ 8

CHAPTER 2 Literature review ..................................................................................... 10

2.1 Introduction ............................................................................................................. 10

2.2 The concepts of vulnerability and resilience ............................................................ 10

2.3 Vulnerability theory ................................................................................................. 11

2.3.1 Economic vulnerability ............................................................................................. 15

2.4 Resilience theory .................................................................................................... 15

2.5 Vulnerability and resilience measurement framework ............................................. 17

2.6 Emerging economies theory ................................................................................... 18

2.6.1 Financial integration ................................................................................................. 19

2.6.2 Amplification effects of exogenous shocks ............................................................... 20

2.6.3 Reactions to shocks in emerging economies ........................................................... 21


v

2.6.4 Emerging economies and foreign direct investment (FDI) ........................................ 22

2.6.5 Economic diversification .......................................................................................... 24

2.7 Summary of literature review .................................................................................. 25

CHAPTER 3 Research hypotheses ............................................................................ 27

3.1 Introduction ............................................................................................................. 27

3.2 Hypotheses ............................................................................................................ 27

3.2.1 Hypothesis 1 Accumulation of international reserves ............................................... 27

3.2.2 Hypothesis 2 Economic concentration ..................................................................... 28

3.2.3 Hypothesis 3 Trade openness ................................................................................. 28

3.2.4 Hypothesis 4 Financial integration ........................................................................... 29

Chapter 4 Research methodology ............................................................................. 30

4.1 Introduction ............................................................................................................. 30

4.2 Research design..................................................................................................... 31

4.2.1 Population................................................................................................................ 31

4.2.2 Unit of analysis ........................................................................................................ 31

4.2.3 Sampling ................................................................................................................. 32

4.2.4 Data gathering process ............................................................................................ 33

4.3 Research instrument .............................................................................................. 34

4.3.1 Accumulation of reserves ......................................................................................... 35

4.3.2 Economic concentration ........................................................................................... 35

4.3.3 Trade openness ....................................................................................................... 36


4.4 Validity and reliability .............................................................................................. 37

4.5 Data analysis approach .......................................................................................... 38

4.5.1 Regression analysis ................................................................................................. 38

4.5.2 Multiple correlation analysis ..................................................................................... 39


vi

4.5.3 Multicollinearity ........................................................................................................ 39

4.6 Research limitations ............................................................................................... 40

Chapter 5 Results ........................................................................................................ 41

5.1 Introduction ............................................................................................................. 41

5.2 Descriptive statistics: The sub-Saharan African (SSA) region ................................. 42

5.2.1 GDP growth ............................................................................................................. 42

5.2.2 Trade openness ....................................................................................................... 44

5.2.3 Reserves accumulation ............................................................................................ 45


5.2.5 Concentration ratio .................................................................................................. 48

5.3 Statistical significance of the resilience and vulnerability model .............................. 49

5.4 Hypothesis testing .................................................................................................. 54

5.4.1 Hypothesis 1 ............................................................................................................ 54

5.4.2 Hypothesis 2 ............................................................................................................ 55

5.4.3 Hypothesis 3 ............................................................................................................ 57

5.4.4 Hypothesis 4 ............................................................................................................ 58

5.5 Multicollinearity results............................................................................................ 59

5.6 Summary of hypotheses results .............................................................................. 61

CHAPTER 6 Discussion of results ............................................................................. 62

6.1 ................................................................................................................................ 62

Introduction .................................................................................................................. 62

6.2 Hypothesis 1 discussion: Accumulation of international reserves ........................... 62

6.2.1 Evaluation of economic performance ....................................................................... 63

6.2.2 Summary of hypothesis findings .............................................................................. 65

6.3 Hypothesis 2 discussion: Economic concentration .................................................. 66



vii


6.4 Hypothesis 3 Discussion: Trade openness ............................................................. 70



6.5 Hypothesis 4 Discussion: Financial integration ....................................................... 76



6.6 Summary of results ................................................................................................. 82

CHAPTER 7 Conclusion .............................................................................................. 84

7.1 Summary of main findings ...................................................................................... 84

7.2 Research recommendations ................................................................................... 86

7.3 Research limitations ............................................................................................... 88

7.4 Recommendations for future research .................................................................... 89

7.5 Conclusion .............................................................................................................. 89

Reference list ............................................................................................................... 91

Appendix A: Detailed country figures for FDI-to-GDP ratio ......................................... 101

Appendix B: Detailed results for Hypothesis 1 ............................................................ 102

Appendix C: Detailed results for Hypothesis 2 ............................................................ 103

Appendix D: Detailed results for Hypothesis 3 ............................................................ 104

Appendix E: Detailed Results for Hypothesis 4 ........................................................... 105

Appendix F: Detailed Results of Multi- Collinearity ..................................................... 106

Appendix G: Detailed Results of Standardised Coefficients ........................................ 110


1

List of figures and tables

Figure 2.1 Definition of vulnerability in terms of climate change ....................................... 13

Figure 2.2 Definition of vulnerability applied to economic conditions ................................ 14

Figure 2.3 The role of economic resilience in economic disasters .................................... 17

Figure 2.4 Financial amplification effects .......................................................................... 21

Figure 2.5 Market institutions required for FDI .................................................................. 23

Table 4.1 Resilience unit of analysis ................................................................................ 32

Table 4.2 Vulnerability unit of analysis.............................................................................. 32

Table 4.3 Economic data types ........................................................................................ 34

Table 4.4 Hypotheses variables ....................................................................................... 35

Table 4.5 Validity and reliability evaluation ....................................................................... 38

Table 4.6 Multiple regression table ................................................................................... 39

Figure 4.1 Multicollinearity VIF ......................................................................................... 40

Figure 5.1 Sample country list .......................................................................................... 42

Figure 5.2 Average sub-Saharan African economies’ GDP growth ................................... 43

Figure 5.3 Average sub-Saharan African economies’ GDP .............................................. 44

Figure 5.4 Average sub-Saharan African economies’ imports .......................................... 45

Figure 5.5 Average sub-Saharan African economies’ total reserves ................................. 46

Figure 5.6 Average sub-Saharan African economies’ current account trade balance ....... 47

Figure 5.7 Average sub-Saharan African economies’ external debt stock ........................ 48

Figure 5.8 Sub-Saharan African economies’ concentration ratio ...................................... 49

Table 5.1 Hypothesis 1: Summary .................................................................................... 55




Table 5.5 Removed economic parameters ....................................................................... 60

Figure 6.1 Hypothesis 1: Mean average current account balance .................................... 64

Figure 6.2 Hypothesis 1: Mean average external debt stock ............................................ 65

Figure 6.3 Hypothesis 2: Mean average exports data ....................................................... 67

Figure 6.4 Hypothesis 2: Mean average GDP .................................................................. 69

Figure 6.5 Hypothesis 3: Mean average GDP .................................................................. 72

Figure 6.6 Hypothesis 3: Mean average exports .............................................................. 73

Figure 6.7 Hypothesis 3: Mean average current account balance .................................... 74


2

Figure 6.8 Hypothesis 4: Mean average GDP growth ....................................................... 78

Figure 6.9 Hypothesis 4: Mean average external debt stocks ........................................... 79

Figure 6.10 Hypothesis 4: Mean average GDP ................................................................ 80

Figure 6.11 Hypothesis 4: Mean average total reserves ................................................... 81

Figure 7.1 Resilience and vulnerability model for sub-Saharan African economies .......... 86

Table A Sample countries FDI-to-GDP ratios ................................................................. 101

Table B Hypothesis 1: Results........................................................................................ 102

Table C Hypothesis 2: Results ....................................................................................... 103

Table D Hypothesis 3: Results ....................................................................................... 104

Table E Hypothesis 4: Results........................................................................................ 105

Table F1 Mauritius: Pearson correlation ......................................................................... 106

Table F2 Mozambique: Pearson correlation ................................................................... 106

Table F3 Ghana: Pearson correlation ............................................................................. 106

Table F4 Lesotho: Pearson correlation ........................................................................... 107

Table F5 Cape Verde: Pearson correlation .................................................................... 107

Table F6 Namibia: Pearson correlation .......................................................................... 107

Table F7 Gambia: Pearson correlation ........................................................................... 108

Table F8 Nigeria: Pearson correlation ............................................................................ 108

Table F9 Morocco: Pearson correlation .......................................................................... 108

Table F10: Rwanda: Pearson correlation ....................................................................... 109

Table F11 South Africa: Pearson correlation .................................................................. 109

Table G Standardised coefficients .................................................................................. 110


3

CHAPTER 1 Introduction to research problem

1.1 Introduction

In the past two decades, the global economy has experienced a series of major economic

shocks in the form of the Asian crisis, the global financial crisis and the eurozone crisis.

These economic crises have had major, long-lasting economic impacts on most

economies, and more so on emerging or developing economies. Ravallion and Chen

(2009) estimated that the financial global crisis compounded the issue by adding 53 million

people living on less than $1.25 a day and 64 million people living on less than $2 a day.

Many of the developing countries were affected by a decrease in capital inflows in the form

of foreign direct investment (FDI). Total capital outflows from European markets to

emerging economies decreased from more than $2 trillion in 2007 to less than $200 billion

which indicated less investment in the form of FDI in emerging economies (Suttle, Koepe

& Tiftik, 2012). Most notably, Fitch (2013) mentioned that for sub-Saharan African (SSA)

economies to create an enabling environment for FDI, it would be necessary to reduce

external vulnerabilities and to increase resilience through economic diversification and

developing local debt and credit markets. This brings to the fore the importance of these

two aspects of economic resilience and economic vulnerability that should function in a

reciprocal nature to attract FDI and to withstand external shocks from foreign markets.

The link between economic resilience and economic vulnerability is an important aspect

when evaluating emerging economies. Emerging economies’ reliance on commodities

exposes them to external vulnerabilities, such as price changes and demand factors, and

these economies need to enhance their resilience against these external vulnerabilities.

1.2 Purpose of the research

The aim of this study was to evaluate and assess the economic vulnerability to and

resilience of emerging economies against adverse economic shocks. The research

focused specifically on the SSA region. The 2007/2008 global financial crisis and the 2009


4

recession that originated in the United States of America quickly spread to other

developed, mainly European, nations due to the integrated nature of the global financial

system (Devarajan & Kasekende, 2011). The spillover effects then spread to a lesser

extent to other economies in Asia and Africa, adversely affecting emerging markets.

Since then, a significant amount of academic research has been undertaken to evaluate

the cause of the global financial crisis, its impact on economies, both developed and

emerging economies, and measures implemented to counter the adverse impact of the

crisis in the form of fiscal and monetary policies. The study of economic vulnerability and

resilience was born out of the need for economies to be able to minimise or withstand the

impact of such shocks. This is of particular importance for the SSA region as economic

resilience and vulnerability of these economies are key determinants of levels of FDI.

Understanding these dimensions for emerging economies is of particular importance

(Cheung & Ito, 2008). It is thus important to evaluate and demonstrate the economic

stability of the region to attract FDI and to be able to provide sufficient returns on

investment.

1.3 Problem definition

Sub-Saharan Africa’s economic performance has been improving steadily according to

Aryeetey and Ackah (2011) who noted that, since 2000, real output in Africa’s economies

has been growing at a rate of above five per cent on average. Indeed, Africa’s growth has

been phenomenal in light of the FDI attracted by SSA countries as well the demand for

their export goods, mainly commodities such as Brent crude oil, copper and coffee. FDI in

the SSA region has played a vital role in stimulating economic activity with net private

capital inflows increasing from US$17.1 billion in 2002 to US$81 billion in 2007 (Aryeetey

& Ackah, 2011). It is also important to note, however, that this growth was spearheaded by

increases in commodity prices where African economies had a competitive advantage

Menson, 2012).

However, the global recession in 2009, caused by the 2007/2008 financial crisis, impeded

the hard-fought-for gains of the region in terms of economic activity and growth. The result


5

was a slump in the economies of the region with some economies affected more than

others, mainly middle-income economies such as South Africa and Mauritius. The global

crisis highlighted the vulnerability of emerging economies, especially to commodity prices,

and growth appears to have been adversely affected by these economic shocks. Hence,

the manner in which economies respond to these shocks has become critical as they can

destabilise economies and derail economic gains.

1.3.1 Impact on foreign direct investment

Aryeetey and Ackah (2011) observed that the portfolio of economic development in the

form of FDI has been reduced in countries that have been perceived as riskier markets

where economic stability cannot be guaranteed. FDI is particularly important for Africa, the

SSA region to be more precise, in that it is seen to be a vehicle to achieve the United

Nation’s Millennium Development Goals (MDGs), namely to halve poverty in the region by

2015 (Asiedu, 2006). The research conducted by Asiedu (2006) further stated that income

levels in the region are inadequate to achieve the GDP growth levels required to achieve

the MDGs. Hence, FDI in emerging economies is a vital source of investment income.

Based on the empirical work done by these authors, it becomes clear that markets that are

perceived as less risky or better able to withstand economic shocks would attract the FDI

required to fund increased economic activity. Economic stability is important not only for

specific countries, but also for the region in which they are situated as there are significant

spillover effects and synergies among countries in a geographical region due to

globalisation and free trade. For example, Managi and Bwalya (2010) explored the

proposition of FDI and technology transfer through technology spillovers. Their research

explored the relationship between inward FDI and productivity levels. Their proposition

was that FDI increases competition in the local markets, hence increasing the local

economy’s productivity levels. A positive correlation was found between foreign firms

operating in local markets through horizontal channels, intra-industry and productivity

(Managi & Bwalya, 2010). In addition to the correlation were regional effects from FDI in

terms of technology and productivity levels in the Kenyan and Tanzanian region. This

indicated that the concentration of FDI in a region can enhance competitive aspects with

regard to the competitiveness of an economy.


6

1.3.2 Emerging economy vulnerability and resilience

The United Nations Secretary General, Ban Ki-moon, speaking at the fifth Tokyo

International Conference on African Development (TICAD) in June 2013, emphasised the

need for African economies to become resilient through diversification during a discussion

of investment as an engine for economic development (UN Secretary General, 2013).

This, then, is a shared view that without diversification, African economies could be

rendered vulnerable to exogenous shocks. The South African Minister of Finance also

indicated that African economies were faced with clear vulnerabilities due to a lack of

diversification (Donnelly, 2013).

The indications from the discussions on African economies at TICAD was that while

investors are indeed looking for investment opportunities, resilient economies are

nevertheless essential to ensure that investments are secure in the long run in those

economies. Ban Ki-moon (UN Secretary General, 2013) further emphasised that due to his

views that responsible foreign investors provide more value in terms of development in

Africa than any other form of development method, it is therefore essential to have

economic resilience in emerging markets.

Moody’s rating reports are often used to measure an economy’s attractiveness to foreign

investors. In recent times, an economy’s resilience has been evaluated by Moody’s to

determine an economy’s rating. For example, a report released in May 2013 reflected a B3

rating for Moldova (Moody’s, 2013). A B3 rating means that obligations are considered

speculative and are subject to high credit risk (Moody’s, 2013). One of the key reasons for

the poor rating was low economic resilience. This highlights the importance of encouraging

investor confidence in the resilience of emerging economies in order to be an attractive

destination for foreign investors.

However, this is not where the story ends. An investment needs to be maintained in an

economy as vulnerability may present itself in the form of “hot capital” resulting from large

private capital inflows due to the structure of the economy. The reversal of these capital

inflows may also present vulnerability (Donnelly, 2013). For instance, according to the

2012 Institute of International Finance’s report, net private capital inflows fell to $910 billion


7

in 2011 from over $1 trillion a year before with a projected recovery to $893 billion for 2013

(Suttle et al., 2012). In this report, Suttle et al. (2012) attributed the shock in capital flows

to transmission mechanisms due to the euro economies’ crisis which demonstrated

vulnerability in terms of connectedness to the European economies.

Hence, research to evaluate the vulnerability and resilience of emerging economies

(specifically in the SSA region) to economic shocks became imperative to:

evaluate the stability of the region

explore its inherent weaknesses and strengths

propose guidelines for the assessment of such vulnerabilities and resilience

provide recommendations on how countries can enhance their investment

attractiveness.

1.4 Context

Rathbone (2013) noted Mexico’s plan to expand its economy by improving its GDP growth

from 4% to 6%. He noted that Mexico’s Reserve Bank Governor mentioned that economic

resilience is one of the aspects that Mexico is investigating in an effort to improve its GDP

growth levels. Economic resilience was mentioned as a necessary condition for GDP

growth levels even though it is not sufficient on its own.

1.4.1 Resilient dynamism

The statement by the Mexican Reserve Bank Governor advances the argument for

economic resilience as a necessary platform for economic growth. Furthermore, the

subject of resilience has gained traction in recent times due to various exogenous shocks

to economies around the world. The World Economic Forum explored the issue at a recent

gathering of countries in Davos in March 2013. The theme of the discussions was resilient

dynamism (Reuttner, 2013). Indeed, the phrase, “resilient dynamism”, seems contradictory

in that resilience suggests an object that withstands pressure and yet dynamism entails

movement and agility. The theme of resilient dynamism was central to the Davos agenda

where it was mentioned in connection with efforts to restore economic growth and


8

confidence in markets facing incipient economic and political shocks (WEF, 2013). In an

article on resilience of financial systems, Reuttner (2013) suggested that economies

should look at shifting their focus from anticipating and preparing for known events to

preparing for unknown events.

1.4.2 Exploring economic resilience

Recently, more and more economists have been exploring the subject of economic

resilience in various economic publications. This is due, in part, to the recent exogenous

shocks suffered by the global financial and monetary systems. Most notably, the 1997

Asian financial crisis and the 2009 global recession have drawn attention to the impact of

these crises and have subsequently initiated debates on how best to shield or minimise

the impact of such exogenous shocks on economies. Panitchpakdi (2013), at the United

Nations Conference on Trade and Development (UNCTAD), underlined the need to

understand the factors that determine economic vulnerability, especially focusing on the

developing economies. In an effort to understand some of these factors, it became

apparent from the outcome of the conference that many countries were using the

accumulation of reserves as a measure to counter instability in the markets. However, this

is a costly policy exercise which many developing countries cannot afford (Panitchpakdi,

2013). The question that then needs to be asked is how emerging or developing countries

could shape their economies to withstand exogenous shocks.

1.5 Conclusion

This chapter highlighted the importance of the macroeconomic stability of developing or

emerging economies through the implementation of policies to enhance economic

resilience and to reduce economic vulnerability. These concepts are principally important

for SSA economies since they rely heavily on commodities which are volatile in nature

while they simultaneously endeavour to develop economies that are sufficiently stable and

sustainable to attract the FDI required to increase economic growth. It was also noted that

emerging economies are vulnerable to exogenous economic shocks. SSA economies

therefore need to formulate and entrench policies that can increase economic resilience


9

and reduce economic vulnerability to achieve economic growth since integration in the

global economy is inevitable.

This subject is also gaining much attention from world economic organisations in light of

the financial crises which have occurred during the past 20 years and which have often

spilled over into other economies due to the integrated nature of the global economy. The

impact of these crises has often been more severe for emerging economies and their

hard-fought-for economic gains have been eroded. Finally, resilient dynamism was

discussed as a theme that emerging economies need to adopt to manage unknown

exogenous shocks as well as the manner in which the economies are structured for agility

with increased resilience and reduced vulnerability in anticipation of unknown economic

events.


10

CHAPTER 2 Literature review

2.1 Introduction

This chapter provides the literature review of the concepts and various constructs of

economic resilience and vulnerability in the context of emerging economies. It also

provides definitions of economic resilience and vulnerability, and explores their

applicability in the context of emerging or developing economies. Further, the chapter

explores the effects of exogenous shocks with a specific focus on economic vulnerability

and resilience concepts and frameworks in the context of emerging economies in light of

their heavy reliance on commodities (Eichengreen, 2002).

Much of the research in this area focused on biological systems and their ability to adapt to

changes. There has been much interest in this area as a result of the recent financial

crises of the past two decades with specific reference to the impact of exogenous shocks

on emerging economies. This chapter also explores the manifestation and amplification

effects of exogenous shocks on emerging economies as highlighted by Korinek (2011).

Finally, this chapter uses frameworks initially designed to explain exogenous shocks in the

context of climate change such as those proposed by Mechler, Hochrainer, Pflug, Lotsch

and Williges (2010), and adapts these frameworks to the macroeconomic context of

resilience and vulnerability.

2.2 The concepts of vulnerability and resilience

As the concept of resilience “originated in environmental studies, it described the biological

capacity to adapt and thrive under adverse environmental conditions” (Christopherson,

Michie & Tyler, 2010, p. 1). The concept has since been applied to various fields, including

regional economics. Guillaumont (2010) acknowledged that the study of macroeconomic

resilience theory first developed over 40 years ago, with a specific focus on developing

countries in light of international prices of primary exports. However, the subject has


11

received less attention since then and has only emerged again as a result of the recent

global financial crises with specific focus on developing countries.

Briguglio (2003) first developed the concept called the “Singapore Paradox” which refers to

a country that is highly exposed to exogenous shocks but is sufficiently resilient to achieve

constantly high economic growth levels. Briguglio, Cordina, Farrugia and Vella (2009)

further studied the Singapore phenomenon in later research and discovered that

Singapore posed a “contradiction that a country can be highly exposed to exogenous

shocks, rendering it economically vulnerable, and yet still manage to attain high levels of

GDP per capita” (p. 230). Briguglio (2003) developed this concept after an extensive study

of Singapore as it is an island that is highly exposed to external economic shocks and yet

is able to maintain a high GDP per capita. Jayaraman (2006) conceded that this concept is

not easily replicable as Singapore is recognised as an economy that is committed to high

economic growth with strong discipline along with restricted political freedom. However,

there are lessons that can be extracted from the Singapore Paradox and that provide

theories and frameworks based on the literature with regard to this area of addressing

vulnerabilities and building resilience into emerging economies.

Pamungkas (2012) defined the two terms, namely vulnerability and resilience –

“vulnerability reflects pre-disaster condition whereas resilience reflects post-disaster

condition” (p. 8). These conditions are conversely reinforcing in nature in that the

improvement of resilience can mitigate or lessen the vulnerability of an economic system

to future shocks. In this research paper, both terms were analysed and integrated into

frameworks which can be used to evaluate the stability of the microenvironment of

emerging economies. This is important, particularly since South Africa and the SSA region

form part of these emerging economies that are starting to trade with each other as in the

case of the BRICS (Brazil, Russia, India, China and South Africa).

2.3 Vulnerability theory

The empirical study of vulnerability has only received attention in recent times and there is

no consensus on the definition of vulnerability (Harttgen & Günther, 2006). Until recently,


12

vulnerability theory had been largely applied to poverty and households’ risks to poverty,

but still with no agreement on the implications of the concept of vulnerability (Harttgen &

Günther, 2006). However, the concept of vulnerability has gained some traction with some

researchers aiming to define it and also to present it as a study that can be applied across

many disciplines. Naudé, Santos-Paulino and McGillivray (2009) defined vulnerability as a

risk to a system that can be applied across various disciplines to households, regions or

countries that can be affected by specific distresses. These distresses would result in a

negative change to the system (Naudé et al., 2009). There is consensus among various

researchers in this field that vulnerability would result in different definitions depending on

the subject to which the vulnerability concept is applied (Hoddinott & Quisumbing, 2008;

Naudé et al., 2009).

More recent studies have attempted to define and introduce frameworks for the

measurement of vulnerability. Mechler et al. (2010) studied vulnerability in the context of

climate change and its impact on households, and viewed the concept as the likelihood of

experiencing stress due to exogenous shocks. Limitations were highlighted in the study

with regard to this narrow view of the concept since its premise was based on climate

change. In economic terms, the risk was highlighted as the risk facing households of falling

into poverty due to idiosyncratic or covariate shocks (Naudé et al., 2009).

More in-depth research into the components of vulnerability revealed underlying instances

that could be viewed as a function of vulnerability. Mechler et al. (2010) stated that

vulnerability was a function of exposure to hazards and sensitivity of the system, while risk

was a potential hazard and its potential consequences, this being exposure and sensitivity.

Clearly, this separated vulnerability and risk, whereas Naudé et al. (2009) viewed risk as

the probability of a hazard negatively affecting a system.

In Figure 2.1, Mechler et al. (2010) defined vulnerability in the context of climate change

that negatively affects an economy which is more highly exposure due to geographical

location. The impact would expose the vulnerabilities inherent in the economy. Adaptation

and mitigation explored the reactionary factors, including social and governmental

responses, which were used intentionally or unintentionally induced to minimise the impact

and to return the system to its normal state.


13

Figure 2.1 Definition of vulnerability in terms of climate change

Source: Mechler, Hochrainer, Pflug, Lotsch & Williges (2010)

Indeed, risk and vulnerability are ambiguous terms. Figure 2.2 was adapted from the

model of climate vulnerability theory in Figure 2.1 (Mechler et al., 2010) to demonstrate

vulnerability in economic terms. The model in Figure 2.2 applies the concept of

vulnerability to economic conditions as a function of the potential impacts along with

society’s capacity to adapt to the changes. Figure 2.2 demonstrates how exposure to

exogenous shocks has a cascading effect at a macroeconomic level. The adaptability of

an economy is thus crucial in minimising the impact of exogenous shocks, along with

mitigation measures which largely rely on fiscal policies.

In the adapted model in Figure 2.2, the global financial crisis was seen as an exogenous

shock instead of climate change. The openness and integrated nature of the economy

(used for illustration purposes) to the global economy makes it possible for the financial

global crisis to be transmitted to such an economy. The impact of this crisis can be


14

transmitted to an emerging economy, as illustrated in Figure 2.2, through the emerging or

developing economy’s dependence on trade with the developed economies. The results

are that trade is reduced for local businesses with the consequence of less income for

these businesses due to less demand for products destined for export markets. Net

impacts would result in businesses retrenching staff or closing down, thus causing an

increase in unemployment. Loss of employment increases the unemployment rate and

reduces economic activity, causing the economy to go into recession. This demonstrates

the vulnerability of an emerging economy due to its trade openness and dependence on

developed markets for trade and economic activity. The ensuing economic recession

would stimulate a policy response from the government concerned. Economic resilience of

the economy would be measures instituted by the government such as fiscal policies that

increase spending on infrastructure projects or stimulus packages to bail out local

businesses in order to retain employees.

Figure 2.2 Definition of vulnerability applied to economic conditions

Source: Adapted from Mechler, Hochrainer, Pflug, Lotsch & Williges (2010)


15

2.3.1 Economic vulnerability

Briguglio et al. (2009) ascribed economic vulnerability “to inherent conditions affecting a

country’s exposure to exogenous shocks” (p. 230). The inherent conditions could manifest

as the size of the country and degree of openness to trade, among other conditions.

Further arguments presented mention the high degree of economic vulnerability of small

island countries due to the openness of their economies to trade (Briguglio et al. 2009).

According to Briguglio et al. (2009), the study of economic vulnerability was first evidenced

in the early 1990s. This was supported by Guillaumont (2010) who mentioned the Asian

crisis, also in the 1990s. Even though the study of economic instability originated some 40

years ago according to Guillaumont (2010), vulnerability economic theory was only

developed later in the light of structural instability symptoms on a macroeconomic level

which were underlined by various economic crises.

Economic and resilience theory often mention exogenous shocks to an economic system

or economy. These exogenous shocks can be categorised into two categories, namely

natural environmental shocks such as earthquakes and floods, and shocks such as

changes in demand for a commodity, trade prices and global crises as noted by

Guillaumont (2010). These latter shocks are the main sources of economic vulnerability.

2.4 Resilience theory

Resilience theory describes a system’s ability to adapt and reorganise itself as a result of a

sudden shock. Dos Santos and Partidário (2011) defined resilience “as the capacity of

socio-ecological systems to support disturbances and reorganise, [and that it] assumes a

crucial role to avoid disruptions and collapses” (p. 1). This definition is congruent with

Rose’s (2009) definition that resilience is the amount of disturbance a system can absorb

without it changing its state. This definition is at the heart of resilience theory as it can be

applied across systems, natural environments and macroeconomics.


16

This subject has received much attention from an ecological and natural disaster

perspective. Rose and Liao (2005) conducted their research in the area of the coping

behaviour of business, the non-linear adaptive response of organisations and community

resilience to natural disasters. Ecologists were the first to embrace the concept of

resilience more than 30 years ago (Rose, 2009). Other empirical research has emerged

since then that has incorporated this term in various studies. Manyena (2009) introduced

the concept of the mobilisation of resources to maintain the structure of a system. From

the research thus far, it is evident that much of the literature in this area focused mainly on

resilience as a function of natural disasters and many studies did not consider other

exogenous shocks, such as a rise in oil prices, food shortages, and financial and global

crises, as a function of resilience.

According to Rose (2009), resilience has three properties that are evident on deeper

inspection. The first property is the reduced probability of failure which refers to mitigation

measures. The second property is reduced consequences from failure and the third is

reduced time to recovery. These three properties illustrate a more in-depth approach to the

two types of resilience systems, namely inherent and adaptive capabilities.

Pamungkas (2012) posited vulnerability as a pre-disaster condition and resilience as a

post-disaster condition. Figure 2.3 illustrates the interrelationship between resilience and

vulnerability, and how mitigation strategies affect the overall economic output focusing on

disaster management systems (Pamungkas, 2012). The model implies that every structure

has inherent resilient qualities which are enhanced by the reduced vulnerabilities in the

structure. Reduced vulnerability also enhances inherent resilience in the structure. In turn,

mitigation measures reduce vulnerabilities and the probability of failure at the most

fundamental level of resilience.


17

Figure 2.3 The role of economic resilience in economic disasters

Source: Rose (2009)

2.5 Vulnerability and resilience measurement framework

There have been developments in the measurement of economic vulnerability and

resilience, for instance the framework developed by Briguglio et al. (2009). The framework

entails grouping countries or regions by classifying them according to specific criteria.

Countries can be grouped into four categories, namely best case, worst case, self-made

and prodigal son. The computation is performed using regression analysis with the inputs

being macroeconomic stability, fiscal deficit, inflation and unemployment, external debt

and macroeconomic market efficiency, among others. Each input looks in detail at

economic ratios for each country or region.

Gnangnon (2012) advocated that this framework be used in economic resilience theory

and noted empirical work done by other authors in deriving models of such analysis.

Subsequently, Gnangnon (2012) pointed to another method first developed in 2003 and


18

revised in 2009 called the vulnerability index. This economic vulnerability index “captures

vulnerability caused by structural factors” (Gnangnon, 2012, p. 8) and the framework is

called the “Economic Vulnerability Index (EVI)” (p. 8). In addition, the author suggested

that this framework could be used to measure the economic structure of an economy that

is beyond the control of policy, but is affected by the global economy.

2.6 Emerging economies theory

Emerging economies, also referred to as developing or transition economies, is a term that

was first developed by Van Agtmael to describe fast-growing economies in the 1970s

(Bluen, 2012). These economies were differentiated from other economies in terms of

economic attributes in that they displayed rapid growth when compared to developing

nations and third-world countries. Hoskisson, Eden, Lau and Wright (2000) defined

emerging economies as economies that satisfy two criteria, namely a rapid pace of

economic development and government policies that favour market liberalisation and the

free market system. Bluen (2012) noted that the emerging market economies that were

originally covered by the description were Asian economies. They were commonly referred

as the Asian Tigers, and included countries such as China, Singapore, South Korea and

Taiwan. These economies have since developed economically as well in the manner in

which they have developed their institutions. However, the term “emerging economies” is

still applicable to economies that are growing rapidly.

Samoilenko and Osei-Bryson (2010) argued that there is no list of economies that are

defined as emerging economies and preferred the term “transition economies”. These

economies are moving from a centralised planning system to a free market system. These

empirical studies noted that transition economies share characteristics of both developed

and less developed economies (Samoilenko and Osei-Bryson, 2010). This view was

supported by various other authors. Bluen (2012) also mentioned that the term “emerging

economies” has become outdated since it was first coined to describe the Asian Tiger

economies that have since emerged. Hence, this term is currently being questioned by

different schools of thoughts as to its relevance.


19

According to empirical studies by Eichengreen (2002), emerging economies have certain

structural features that define them. The author cited structural features such as using

commodities to drive economic growth, large capital inflows and unpredictability in the

economies themselves. However, each of these features could potentially introduce

vulnerabilities in an emerging economy since many of these features are externally

orientated. Even though the term “emerging economies” was derived more than three

decades ago, there are few studies that define emerging economies or that provide clear

guidelines on how to ascertain whether an economy is classified as an emerging

economy, including the three characteristics identified as rapid growth in GDP, capital

flows and certain unpredictability (Eichengreen, 2002).

2.6.1 Financial integration

The concept of financial integration refers to various links that an economy has to capital

economies (Mougani, 2012). Empirical research acknowledged that financial integration

has two major benefits, these being the allocation of capital across capital economies and

the ability to enable countries to share or reduce risk by reducing consumption volatility

(Kose, Prasad & Terrones, 2006). Financial integration occurs in many forms and some

examples may include, but are not limited to, financial systems, internationalisation of

financial assets and liabilities, banking systems and market institutions (Mougani, 2012).

Kose et al. (2006) argued that financial integration is critical for emerging economies as it

provides access to capital for emerging economies to enable them to diversify their

production efforts. In time, financial integration leads emerging economies to specialisation

which results in competitive advantages.

However, the production specialisation of emerging economies results in industry-specific

vulnerabilities although macroeconomic volatilities in emerging economies cannot be

clearly linked to financial integration according to Kose et al. (2006). Korinek (2011)

concurred with this idea that emerging market economies that are integrated in the global

economy face vulnerabilities. Further, Korinek (2011) stated that these economies are

exposed to the boom-bust cycles of the global economy, especially since these economies

are heavily reliant on capital inflows into their economies


20

There are various arguments as to the type of impact financial integration has on emerging

economies. On the one hand, in a study conducted on SSA economies, Kose et al. (2006)

found links of positive economic growth in developing countries that are financially

integrated and also found other attributes that emerge apart from economic growth, such

as foreign direct investment, skills transfer and technology spillovers. On the other hand,

Mougani (2012) stated that there has been no finding that financial integration results in

positive effects on economic growth for emerging economies.

2.6.2 Amplification effects of exogenous shocks

Demir (2009) noted that developing countries experienced significant capital flows during

the 1990s. Due to the interest of emerging economies in the global economy, there is a

heightened concern about reversals in the capital flows. This can also be seen as a

vulnerability for emerging economies as it could quickly turn into a financial crisis for these

economies (Choi, Sharma & Strömqvist, 2009). Empirical research by Korinek (2011)

produced a model that demonstrated the amplification effects that may be caused an

exogenous shock to an emerging economy. Since Eichengreen (2002) had established

that emerging economies were heavily dependent on commodities, Korinek (2011) created

a model, demonstrated in Figure 2.4, where he stated that when such an emerging

economy experiences a shock in the form of a decline in the aggregate demand of a

commodity, the exchange rate would fall and the asset price would fall. This would result in

adverse balance sheet effects. Korinek (2011) called this financial amplification as such a

shock could also result in the emerging economy not being able to access external finance

which would force a cut-back in spending. In turn, a feedback loop is created in the

economy which amplifies the effects of an exogenous shock.


21

Figure 2.4 Financial amplification effects

Source: Korinek (2011)

Korinek (2011) saw the financial amplification effects as a by-product of external financing

and, as such, they introduced financial fragility into emerging economies. Recently, Choi et

al. (2009) noted that emerging economies had accumulated more financial reserves when

compared with their more developed counterparts as measured by the average reserves-

to-GDP ratio.

2.6.3 Reactions to shocks in emerging economies

The stockpiling of reserves occurred directly after the Asian crisis. This action was taken

by emerging economies to reduce their vulnerabilities to exogenous shocks. Choi et al.

(2009) revealed that emerging economies that were concerned about an exogenous shock

in the form of a sudden stop to capital flows have been using net capital flows to build up

their reserves since these countries do not have the same access as advanced economies

to capital at relatively low costs. This action can be viewed as a resilience mechanism

which these emerging economies have built into their economic systems.

Demir (2009) concluded that the reason for the stockpiling was the volatility of capital flows

to emerging economies as this volatility affected investment by changing the prices of

goods which distorted price signals. This view was also supported by Eichengreen (2002)


22

who stated that the openness of emerging economies had a direct impact on the

exogenous shocks experienced by these economies as they were vulnerable to

commodity and financial shocks. This argument was not supported by research studies

conducted by Duval, Elmeskov and Vogel (2007) who established in their research on the

Singaporean economy that openness actually enhances resilience and reduces economic

vulnerability.

Demir (2009) concluded that during the past few decades, there had been volatility in the

micro and macro environments globally that had had an impact on emerging economies in

the form of boom-bust cycles, lower investments and a reversal of funds from developing

to developed countries. These conclusions were in line with those of Choi et al. (2009)

who stated that advanced economies have better access to capital markets as they can

withdraw capital investment made in other countries, for example, while emerging

economies do not have such options available to them and as such must accumulate large

capital reserves. However, Korinek (2011) argued against this notion of building up

precautionary reserves in favour of reducing them and increasing the amount of borrowing.

This notion requires certain shifts in an emerging economy in that Korinek (2011)

suggested shifting labour into the tradable sector to appreciate the country’s exchange

rate and to relax its borrowing constraints. In conclusion, Korinek (2011) advocated that

emerging economies should “internalize such externalities and coordinate the actions of

market participants toward a lower level of financial fragility” (p. 557).

2.6.4 Emerging economies and foreign direct investment (FDI)

FDI has increasingly played an important role in the development and growth of many

emerging economies. Wright, Filatotchev, Hoskisson and Peng (2005) noted that a key

characteristic of emerging economies is the significant amount of FDI they attract. As

such, there has also been a change in attitude in emerging economies towards FDI

(Gerschewski, 2013). These economies have begun to appreciate the role which FDI plays

in the development of an economy, not only in monetary terms but also in terms of

spillover effects. As noted by Gerschewski (2013), governments of emerging economies

welcome these investments since they also boost the productivity of local firms. Adewumi


23

(2006) studied the impact of FDI on emerging economies’ GDP and concurred that FDI

has a positive contribution and that it was the most resilient factor during the Latin-

American financial crisis in the 1980s and also during the 1997/1998 Asian crisis. These

studies highlighted the importance of FDI in emerging economies and as such resilience

and FDI in emerging economies is of paramount important to sustain high GDP growth

levels. It was also noted that emerging economies with high and resilient growth levels

attract higher levels of FDI than emerging economies that cannot achieve high growth

levels and have not proved to be resilient during economic downturns (Adewumi, 2006).

However, there are challenges such as institutional voids that need to be overcome by

companies wishing to invest in emerging economies such as India or China (Khanna &

Palepu, 2011). Examples of such institutions are illustrated in Figure 2.4. These institutions

are crucial for businesses which want to operate in a particular country. Khanna and

Palepu (2011) noted that globalisation does not eliminate these institutional voids. Rather,

the macroeconomic context of an emerging economy in the form of its political and social

systems would eliminate these institutional voids.

Figure 2.5 Market institutions required for FDI

Source: Khanna and Palepu (2011)


24

2.6.5 Economic diversification

Economic diversification has long been a study of interest to economists with the aim of

promoting economic stability. Seigel, Johnson and Alwang (1995) defined economic

diversification as “the process of structural transformation as resources are shifted out of

primary (natural resource-based) sectors into secondary (manufacturing) and tertiary

(services) sectors.” (p. 263). Mayer (1996) argued against the notion of economic

diversification in that the concept of diversification, even though it is a mechanism to

reduce risk, tends to erode an economy’s competitive advantage.

It has been established that emerging economies, along with those of the SSA region, are

heavily reliant on commodities due to an abundance of natural resources (Eichengreen,

2002). As such, exports and commodity prices are critical to economic growth. Hosein

(2010) put forward a case for economic diversification and the reduction of economic

concentration for mineral-rich countries, more so for emerging economies, to reduce the

impact of exogenous shocks in the form of prices of minerals resulting from cyclical and

volatility changes in economies. These observations gained support from Papageorgiou

and Spatafora (2012) in their studies, with economic reform recommendations to promote

macroeconomic stability through diversification. The underlying assumptions are that

structural transformational processes result in an imbalance in favour of certain sectors

against others. Their findings concluded that economies with more diversified economic

structures faced less volatility in international prices and levels of consumption, and further

increased their economic resilience to external shocks. The argument was explored further

to enhance resilience through geographic diversification of export goods to reduce the

impact of regional economic failures on emerging economies (Papageorgiou & Spatafora,

2012).

Economic diversification is a process that requires human and physical capital becoming

more mature in terms of skills for the structural transition of an economy from exporting

unprocessed goods to exporting manufactured goods (Mayer, 2006). Emerging economies

are therefore faced with this challenge of diversifying their economies by upgrading their

human capital in terms of the skills required for the manufacturing or processing of primary

resources into manufactured goods.


25

2.7 Summary of literature review

Previous studies introduced the concepts of resilience and vulnerability, focusing on

developing or emerging economies, more so on Asian economies. These studies were as

a result of the Asian crisis that engulfed the Asian economies and how they set out to

reduce their vulnerabilities to exogenous shocks and enhance their resilience to unknown

shocks. Examples of Asian economies that managed to attain high levels of growth were

mentioned in previous studies, particularly Singapore as its economy defied its high

exposure to exogenous shocks. As a result, Briguglio (2003) devised a concept termed the

Singapore Paradox.

A previous research study approached the concept of vulnerability from its most

fundamental level. The study revealed that vulnerability is a function of exposure to

exogenous shocks and the economy’s sensitivity to these shocks, which then incorporated

the resilience dimension. There was consensus from all previous research studies in this

field that all economies are vulnerable to exogenous shocks and much attention was paid

to reducing these vulnerabilities and increasing resilience while not restricting the

economies’ ability to achieve high economic growth.

Emerging or developing economies were identified as economies that achieve high

economic growth and are able to attract significant FDI. These economies were also

identified as economies that are at high risk and are highly exposed to exogenous shocks.

In addition, many of these emerging economies lack control mechanisms to cope

effectively with these shocks. The paradox that is facing emerging or developing

economies is that as much as they attract significant FDI, investors still prefer economies

that are achieving high growth rates and are stable and less volatile. Previous research

revealed that Asian economies managed to build resilience and reduce their vulnerabilities

to exogenous shocks through diversifying their economies, building reserve buffers, being

less dependent on borrowings and managing their trade openness while ensuring their

financial openness to enable financing of their international reserves. The end result was

the attraction of significant amounts of FDI and an ability to achieve high economic growth

rates.


26

Evaluated research literature lacked findings on SSA economies when incorporating the

resilience and vulnerability concepts. This research aimed to contribute to the body of

research in this area and to provide a case for SSA economies.


27

CHAPTER 3 Research hypotheses

3.1 Introduction

The aim of the study was to evaluate the economic resilience and vulnerability of emerging

markets with a specific focus on a select few economies, the selection of which was based

on the criteria discussed in Chapter 4. The hypotheses were based on the literature review

from Chapter 2 where certain features of economies were highlighted that might render

economies resilient or vulnerable. The hypotheses aimed to establish whether any

relationships exist between the two concepts and the reciprocal relationships that

ultimately drive economic performance when emerging economies experience economic

shocks.

3.2 Hypotheses

3.2.1 Hypothesis 1 Accumulation of international reserves

Korinek (2011), Choi et al. (2009) and Demir (2000) suggested that emerging economies

were stockpiling reserves in an effort to build resilience against exogenous shocks. They

concluded that these reserves were built to withstand volatilities in the global economy.

Hence, the first hypothesis evaluated this notion for emerging economies.

Null hypothesis ( ):

Accumulating international reserves or stock-piling is a useful means of building resilience

for emerging economies.

Alternative hypothesis ( ):

Accumulating international reserves or stock-piling is not a useful means of building

resilience for emerging economies.


28

3.2.2 Hypothesis 2 Economic concentration

Demir (2009) suggested that emerging economies are susceptible to exogenous shocks in

the form of boom-bust cycles and commodities. This observation combined with the notion

that emerging markets are mainly export-driven economies (Duval et al., 2007) suggests

that economic diversification or lack of sector concentration becomes a critical factor in

building a resilient emerging economy.


Reducing economic concentration is a useful means of building resilience for emerging

economies.


Reducing economic concentration is not a useful means of building resilience for emerging

economies.

3.2.3 Hypothesis 3 Trade openness

In Chapter 2, it was mentioned that the degree of openness to economic trade renders an

economy vulnerable (Briguglio et al. 2009). This view was also supported by Eichengreen

(2002) in that the openness of an emerging economy exposes the economy to exogenous

shocks in the form of commodity shocks. However, Duval et al. (2007) argued against

these observations by citing Singapore in what was termed the Singapore Paradox as this

country has a high degree of openness with an export-driven economy and yet built a high

degree of resilience.


Trade openness to the global economy does not render emerging economies vulnerable to

exogenous shocks.


Trade openness to the global economy renders emerging economies vulnerable to

exogenous shocks.


29

3.2.4 Hypothesis 4 Financial integration

Korinek (2011) mentioned in his study that the level of integration of an emerging economy

in the global economy exposed it to various exogenous shocks such as boom-bust cycles.

However, Duval et al. (2007) argued against this observation, using Singapore as a case

in point.


Financial integration in the global economy renders emerging economies vulnerable to

exogenous shocks.


Financial integration in the global economy does not render emerging economies

vulnerable to exogenous shocks.


30

Chapter 4 Research methodology

4.1 Introduction

The research methodology was based on a quantitative design using secondary data as a

data source. A quantitative approach was selected as an appropriate tool since the

performance data on the macroeconomic level was in the form of numerical performance

indicators. These data were obtained from multiple sources to form a new data set

(Saunders & Lewis, 2012). The main reason for this approach was that relevant

macroeconomic performance indicators were embedded in different databases.

The data were collected over a period of time. Hence, longitudinal data were selected

since the study intended to track and analyse selected variables used in the four

hypotheses. The selected variables were then observed while tracking the performance of

selected economies as the economies experienced economic shocks. A longitudinal

analysis was performed over a period of 17 years from 1995 to 2011 inclusive. This

allowed the capture of economic performance during the normal phase of the global

economy when economic output of the selected countries was optimal as well as the

capture of economic performance during the recent global financial crises that affected

most emerging economies.

Saunders and Lewis (2012) introduced the research onion in 1997 to describe the

unpacking of different layers in the research process. This process was used to chart the

research design and methodology. The inner layer entailed the data collection method. For

this research, secondary data sources were used to collect data. Mixed data methods

were used as the data were obtained from different sources which conformed to the

research hypotheses and also to cross-reference the accuracy of the data sources.

The research type was explanatory as described by Saunders and Lewis (2012) in that it

“takes a descriptive research a stage further by looking for an explanation behind a

particular occurrence through the discovery of causal relationships between key variables”

(p. 113). The aim of this research was to find causal relationships between variables


31

through the analysis of the hypotheses stated in Chapter 3 and a historical analysis was

thus deemed to be the appropriate method.

4.2 Research design

The research design was based on the deductive approach which “involves the testing of a

theoretical proposition by using a research strategy specifically designed for the purpose

of its testing” (Saunders & Lewis, 2012, p. 108). Research hypotheses and existing

economic vulnerability and resilience theories and frameworks were used to obtain

verification or otherwise of the stated hypotheses using the stated theories and

frameworks.

4.2.1 Population

Population was defined as “the complete set of group members” by Saunders and Lewis

(2012, p. 132) and is also known as the universe. For the relevance of this study, all SSA

economies formed part of the population. There are 48 SSA countries and therefore this

was taken as the population of the study (World Bank, 2011).

4.2.2 Unit of analysis

A unit of analysis could be an organisation, system or artefact of the study (Hasan and

Banna, 2010). The research focused on two units of analysis, namely the resilience of an

economy and the vulnerability of an economy. Each of these units of analysis contained

measurements as listed in Table 4.1 and Table 4.2 respectively.


32

Table 4.1 Resilience unit of analysis

Resilience

Variables Ratio Unit

Capital reserves or stock-piling Reserve-to-GDP ratio Percentage

Economic concentration Economic concentration ratio Index

Table 4.2 Vulnerability unit of analysis

Vulnerability

Variables Ratio Unit

Economic openness Import and export-GDP ratio Percentage

Financial integration Assets and liabilities-to-GDP ratio Percentage

4.2.3 Sampling

According to Saunders and Lewis (2012), a sample is a subgroup of the population or the

selected universe. The selected population of the study was all economies in the SSA

region. This region was selected as a case for study due to the growing FDI activity in the

region, with the region’s global share of FDI rising from 3.2% in 2007 to 5.6% in 2012

(Ernst & Young, 2013). The study was mainly interested in these emerging economies

since they maintain trade partnerships with other economies through these links.

Since a complete list of countries or populations was obtained, probability sampling

techniques were used (Saunders and Lewis, 2012). The total list of countries that were

listed in the population was 48 SSA economies. A systematic probability sampling

technique was selected so that the countries could be ranked according to their

attractiveness to FDI relative to the size of their economies. The study was interested in

assessing the resilience and vulnerabilities of emerging economies by evaluating their

economic performance against exogenous shocks, using the 2007/2008 financial global

crisis as a contextual milieu and their ability to attract FDI inflows under these conditions.


33

A systematic sampling method was selected. Pepe (1997) stated that systematic sampling

involves a random ordering of the population in the variable values used. This method is

considered equivalent to a random sample. The sample focused on the economies that

attracted the highest FDI relative to the size of their economies as this was an indication

that these economies were forming trade links with the global economy. To eliminate the

size of the economy as a criterion, FDI was viewed in relation to the size of the economy.

Hence, FDI was reflected as a percentage of each country’s GDP (FDI-to-GDP), using

2011 figures. The top 20 economies in relation to FDI-to-GDP (as a percentage) in the

SSA region were selected as part of the sample for analysis. This method was random in

that it did not consider any internal characteristics or mechanisms that enforced resiliency

and reduced or enhanced inherent vulnerabilities. The sampling method was not biased

towards any internal characteristics possessed by these economies such as the availability

of resources or towards resource-rich countries. Hence, the sampling was truly random in

the countries selected for analysis.

4.2.4 Data gathering process

Secondary data were selected as a data source as this type of data source allowed access

to larger data sets than could be collected by this researcher (Saunders & Lewis, 2012).

This method also saved time in the process of data collection. As per Saunders and Lewis

(2012), collecting secondary data is unobtrusive, can easily be combined with other data

sets and is open to public scrutiny.

4.2.4.1 Data sources

Data were collected from two major reputable sources which were the World Bank for the

macroeconomic country performance data and United Nations Conference on Trade and

Development (UNCTAD) for the trade export concentration ratio which was composed of

trade export data for each country. The data collection involved the collection of data sets

of the entire population of 48 SSA countries from the World Bank and UNCTAD. Economic

data for Asian economies were collected from the Economist Intelligence Unit (EIU).


34

4.2.4.2 Data types

The analysis of the data, through the use of the instruments, required specific economic

parameters that were used and transformed to form input variables. The data, as

illustrated in Table 4.3, were collected for each of the emerging economies as input

variables and applied to specific purposes as detailed in the purpose column.

Table 4.3 Economic data types

Parameter no Economic parameter Unit Input variable Purpose

1 GDP US dollars FDI-to-GDP ratio Sampling, Research instrument

2 Foreign direct investment

US dollars FDI-to-GDP ratio Sampling

3 Country imports US dollars Economic trade openness

Research instrument

4 Country exports US dollars Economic trade openness

Research instrument

5 Foreign reserves US dollars International trade reserves

Research instrument

6 Country sector data Percentage Concentration ratio Research instrument

7 Country external assets

US dollars Financial integration

Research instrument

8 Country external liabilities

US dollars Financial integration

Research instrument

4.3 Research instrument

The research instrument consisted of methods adopted from various publications due to

the nature of the hypotheses that were posed in Chapter 3. The appropriate instrument

was allocated for each hypothesis to process the input variables through the proposed

method based on empirical work to produce an output variable for analysis. Table 4.4


35

summarises the hypotheses, the measurement variables used and the input data used for

each measurement variable.

Table 4.4 Hypotheses variables

Hypothesis no. Hypothesis Measurement variable Input data

1 Accumulation of reserves

International reserves Foreign reserves (US$), GDP (US$)

2 Economic concentration

Herfindahl-Hirchman Index

Economic sector data

3 Trade openness

Trade openness Import (US$), Export (US$), GDP (US$)

4 Financial integration

Financial integration Assets (US$), Liabilities (US$), GDP (US$)

The instruments that were used for each research question are discussed below.

4.3.1 Accumulation of reserves

The instrument that was used to obtain international reserves was that formulated by

Hur and Kondo (2013) where foreign reserves are all foreign assets, except for gold, that

are controlled by the state. The instrument used was formulated by Cheung and Ito (2008)

and was stated as:

=

The input variables to the instrument were the foreign reserves (excluding gold), calculated

as a percentage of GDP at a point in time and measured in US dollars.

4.3.2 Economic concentration

There have been many models developed over the years to measure economic industry

concentration or diversification, for instance the concentration ratio (Rhoades, 2011).

However, the Herfindahl-Hirchman Index (HHI) has become the primary measure that was


36

developed to measure the concentration of firms or industries in an economy (Calkins,

2002). This instrument was used to measure an economy’s concentration and was

denoted as:

∑( )

The input variables to the instrument were the industry sector sizes of the economy, with

the larger industries contributing more to the Herfindahl-Hirchman Index or concentration

coefficient.

The economic diversification instrument was the inverse of the HHI as stated below:

=

This instrument highlighted the diverse nature of the economy.

4.3.3 Trade openness

Research was conducted by Cheung and Ito (2008) on modelling an instrument that could

be used to measure an economy’s average propensity to import and export, and which

measures the economy’s openness to the global economy. This model was supported by

Down (2007) and determines that trade openness is reflected as imports plus exports as a

percentage of an economy’s GDP. The formula to measure an economy’s trade openness

is stated below:

= ( )

This formula consisted of input variables of an economy’s import, export and GDP values

measured in US dollars.


37


The model that was constructed to measure financial integration was developed by Lane

and Milesi-Ferretti (2006) and offered a volume-based measure of international financial

integration. This instrument was further developed by Choi et al. (2009) to estimate the

effects of financial integration on emerging economies. The instrument is stated below:

= ( )

This instrument consisted of input variables of the stock of external assets (FA), as

measured by the current account balance (Lane and Milesi-Ferretti, 2003) with the stock of

external liabilities (FL) and the economy’s GDP, as measured in dollars.

4.4 Validity and reliability

Validity of the research methodology refers to the accuracy of the method used to

measure what it is supposed to measure, while reliability refers to the consistency of the

results should the process of measurement be repeated (Saunders & Lewis, 2012). The

evaluation of vulnerability and resilience of emerging economies against exogenous

shocks was evaluated in the context of the FDI that SSA economies were able to attract as

this singularity reflected the economies’ integration into the global economy and is one of

the factors that drives economic growth in these economies.

The performance of the SSA economies’ vulnerability and resilience against exogenous

shocks was evaluated in the context of the recent financial global crisis that affected all

economies across all continents. Table 4.5 summarises the evaluation of the research

methodology’s validity and reliability.


38

Table 4.5 Validity and reliability evaluation

Factor Evaluation

Subject Selection

Size of the countries’ GDP was not a factor, along with the internal structuring of the economies

History There are no specific events that occurred during the history of the research

Testing Quantitative data were collected over the same period from the same sources (World Bank, UNCTAD) for all countries sampled

Subject error Data were collected over the same period

Subject bias Data reflected quantitatively from the input data to the output data

4.5 Data analysis approach

Data analysis was performed using two methods for each hypothesis. The output

measurement of the impact of the exogenous shocks was based on the economies’ output

GDP. Hence, this was treated as an independent variable for both approaches. The first

method of regression analysis determined whether there was a relationship between each

of the four hypotheses. The second method of multiple correlation analysis determined the

strength of correlation between each of the four independent variables and the dependent

variable, namely the GDP output.

4.5.1 Regression analysis

Weiers (2010) stated that regression analysis provides a “best fit” mathematical equation

for two variables. This type of regression analysis involved the use of a dependent (y) and

independent (x) variable to perform correlation analysis, of which the strength of the

relationship between the variables was measured. As mentioned by Weiers (2010), since

the research study was evaluating multiple variables, a multiple regression and correlation

approach was more appropriate and was used for the analysis.

A linear relationship was examined with economic GDP growth as the dependent variable

and the four variables as independent variables. The multiple regression analysis was

computed as reflected in Table 4.6.


39

Table 4.6 Multiple regression table

Dependent variable (y) Independent variables (x's)

GDP growth

International reserves

Economic concentration

Trade openness

Financial integration

Each of the linear multiple regression tests were computed at a 95% confidence level to

test for significance of the variables in determining the output GDP growth.

4.5.2 Multiple correlation analysis

The coefficient of multiple determinations (R2) was calculated as the variation in the GDP

growth (dependent variable) that was explained by the multiple regression equation with

the independent variables (Weiers, 2010). The R2 was calculated for each of the sample

SSA countries over a period of 17 years, from 1995 to 2011, using the multiple regression

equation to evaluate which of these economies’ GDP growth rate was explained by the

four independent variables.

4.5.3 Multicollinearity

Multicollinearity occurs when one or more independent variables are highly correlated with

one another in a multiregression analysis (Ethington, 2005). This was a critical

phenomenon in that highly correlated independent variables caused by type 2 errors in the

interpretation of the regression analysis result, and this may lead to inaccurate coefficients

estimates and standard errors which may lead to inference errors (Grewal, Cote &

Baumgarter, 2002). Lynch (2003) used the variance inflation factor (VIF) to explore the

issue of multicollinearity as highly correlated independent variables will render the VIF very

large. Lynch (2003) indicated a threshold VIF value of 10 to be the threshold value for

independent value correlation which corresponds to an R2 regression of 0.9 correlation

value. Beyond this value, the regression becomes unstable as illustrated in Figure 4.1.


40

The VIF value used as a threshold to detect multicollinearity was defined as 10 and any

variable equivalent to an R2 of 0.9 was removed from the regression analysis to minimise

multicollinearity.

Figure 4.1 Multicollinearity VIF

4.6 Research limitations

There were limitations to using secondary data for the research in that the data might not

be completely suitable for the research hypotheses and would have to be processed

through various research instruments since the data were originally collected for a different

purpose to this research study. In addition, there might also be questions around how the

data were collected by the sources (Saunders & Lewis, 2012). The research also focused

on SSA economies in the context of inward FDI to assess the resilience and vulnerability

of these economies. As such, the selection criteria might not be applicable to all emerging

economies since the selected emerging economies attracted high levels of inward FDI.


41

Chapter 5 Results

5.1 Introduction

Chapter 4 detailed the methodology that was used to collect and analyse the data. This

chapter presents the results and findings of the collected data, and also presents results

based on the hypotheses posed in Chapter 3. The secondary data were collected from

credible sources, namely the World Bank, the United Nations Conference on Trade and

Development (UNCTAD) and the Economic Intelligence Unit (EIU).

The aim of the research study was to evaluate emerging economy resilience to and

vulnerability against adverse economic shocks using the SSA region as the case in point.

A sample was used of 20 performing countries in terms of attracting FDI as a percentage

of each economy’s GDP. This removed the bias of focusing only on the size of the

economies. In addition, the research study was undertaken against the backdrop of the

importance of attracting FDI and maintaining return on investment through measures that

seek to address economic structural issues pertaining to the economies’ resilience to and

vulnerabilities against exogenous economic shocks.

The data were collected and analysed over a 17-year period from 1995 to 2011. The 17-

year period allowed the study to track the economic changes through the various phases

within the context of global economic performance, including important periods such as the

most recent global recession. The data also allowed the study to evaluate the trends of

these economies with respect to trade openness, financial integration in the global

economy, economic or GDP growth, and trade diversification. These were important

issues to explore and form part of the rationalising of the results in Chapter 6 as well as

the recommendations for the SSA region in improving economic resilience while reducing

vulnerability against shocks that are beyond their sphere of control.

The next section describes the economic trends observed from the collected data for the

SSA region as a collective.


42

5.2 Descriptive statistics: The sub-Saharan African (SSA) region

This section describes the economic performance trends for the SSA region as a whole

over a 17-year period (1995 to 2011) for the 20 selected countries. Figure 5.1 illustrates

the SSA sampled countries based on their FDI-to-GDP ratio. Refer to Appendix A for

detailed figures.

Figure 5.1 Sample country list

5.2.1 GDP growth

The collected dataset of 20 SSA economies over a 17-year period indicated boom-bust

economic growth with mild volatility levels during the period under observation. It was

evident from the mid- to late 1990s that SSA economies’ GDP output reached levels as

high as 10%. However, there was an immense decline in 2001 with GDP levels dropping


43

as low as 3% before a recovery of around 6% during the first decade of the twenty-first

century.

There was a decline in economic growth across SSA economies during the 2008/2009

global recession although the impact appears to be less than the slump of 2001/2002 as a

result of the recession in the US economy, as demonstrated in Figure 5.2.

Figure 5.2 Average sub-Saharan African economies’ GDP growth

According to Figure 5.3, it was evident from the data collected that GDP levels for the

sampled SSA economies from the mid-1990s to the early twenty-first century were

stagnant based on the average mean of these economies. From the early 2000s, SSA

economies experienced sharp increases in the sizes of their GDPs with slight GDP

contractions in 2009 due to the global financial crisis with signs of economic recovery from

2010.


44

Figure 5.3 Average sub-Saharan African economies’ GDP

5.2.2 Trade openness

The dataset collected revealed that imports in the SSA economies coincided with their

economic growth levels as imports experienced a sharp increase from the early 2000s

until the economic recession in 2008/2009, according to the dataset compiled in Figure

5.4.

As illustrated in Figure 5.4, trade exports revealed similar trends to imports as SSA

economies started exporting more commodities and services to the global economy from

mid-2000. However, it appeared that exports from these economies were affected slightly

more than imports as SSA economies traded with the global economy that demanded less

of their products. As of 2011, the data revealed that SSA economies’ imports and exports

had fully recovered and were well above the previous levels prior to the global recession

even though they were now importing more than they were exporting.


45

Figure 5.4 Average sub-Saharan African economies’ imports

5.2.3 Reserves accumulation

The dataset revealed average total reserves (as held by the IMF in terms of drawing

rights) for SSA economies from the mid-1990s as reflected in Figure 5.5. What was

evident from the World Bank dataset was that the SSA’s total reserves increased

dramatically from the early 2000s which demonstrates a similar trend to the average size

of SSA GDP economies. It appeared that the 2008/2009 economic recession had a minor

impact on the average total reserves as SSA economies drew on their total reserves.

0

5

10

15

20

1995 2000 2005 2010

Avg

Inp

ort

s -

Avg

Exp

ort

s (U

S$ b

illio

n)

Year

Average mean sub-Saharan African imports and exports

AvgExports AvgImports


46

Figure 5.5 Average sub-Saharan African economies’ total reserves


The collected dataset suggested that SSA economies were functioning with trade deficits,

based on the country mean average, until the year 2000. According to Figure 5.6, in 2005,

however, the SSA economies had, on mean average, the highest trade surplus and had

accumulated significant external assets of approximately US$1 billion. Since 2007, the

World Bank data indicated that the SSA economies have been running average mean

trade deficits and there is no sign of the current account balance turning positive as the

trend indicates that these economies are continuing to have trade deficits. This could be

attributed to the flow of FDI into these economies.


47

Figure 5.6 Average sub-Saharan African economies’ current account trade balance

According to Figure 5.7, SSA economies’ average external debt stocks have been on the

rise for the past 17 years. The early 2000s and late 2000s dataset suggested a slight curb

on external debt levels with an exponential increase in debt levels on average for SSA

economies.


48

Figure 5.7 Average sub-Saharan African economies’ external debt stock

5.2.5 Concentration ratio

The concentration ratio data (UNCTAD, 2012) that detailed the lack of export

diversification in SSA individual economies revealed highly concentrated economies in

1995. By the early 2000s, the average concentration ratio had decreased by a quarter,

suggesting that SSA economies had diversified their export sectors. Throughout the mid-

2000s, the data revealed that the concentration ratio had increased and by 2010 it had

dropped to its lowest levels throughout the SSA economies, according to Figure 5.8.


49

Figure 5.8 Sub-Saharan African economies’ concentration ratio

.

5.3 Statistical significance of the resilience and vulnerability model

The resilience and vulnerability model, with GDP growth as the dependent variable (y) and

economic openness, economic integration, economic concentration and international

reserves as independent variables (x), was compiled for all 20 countries over the 17-year

period. The resilience and vulnerability model was statistically significant for eight out of

the 20 countries. The model was statistically significant for the following countries:


50

Mauritius – International reserves removed

The model was statistically significant for Mauritius with a statistical significance of 0.0001 at a 95% confidence level (0.0001 < 0.05)

with a correlation of 0.791.

Model summary

Model R R2 Adjusted R

2 Std. error of the

estimate Change statistics

country = Mauritius

R2 change F change df1 df2 Sig. F change

1 .911a .830 .791 .86415 .830 21.182 3 13 .000

Cape Verde – International reserves and financial integration removed

The model was statistically significant for Cape Verde with a statistical significance of 0.023 at a 95% confidence level (0.023 < 0.05)

with a correlation of 0417.

Model summary


2 Std. error of the


country = Cape_Verde


1 .646a .417 .334 1.74794 .417 5.003 2 14 .023


51

Lesotho – Trade openness and international reserves removed

The model was statistically significant for Lesotho with a statistical significance of 0.001 at a 95% confidence level (0.001 < 0.05)


Model summary


2 Std. error of the


country = Lesotho


1 .789a .623 .569 1.09824 .623 11.566 2 14 .001

Mozambique – International reserves removed

The model was statistically significant for Mozambique with a statistical significance of 0.002 at a 95% confidence level (0.002 <

0.05) with a correlation of 0.66.

Model summary


2 Std. error of the


country = Mozambique


1 .813a .660 .582 1.71559 .660 8.423 3 13 .002


52

Nigeria

The model was statistically significant for Nigeria with a statistical significance of 0.006 at a 95% confidence level (0.006 < 0.05) with

a correlation of 0.673.

Model summary


2 Std. error of the


country = Nigeria R2 change F change df1 df2 Sig. F change

1 .820a .673 .564 1.90957 .673 6.168 4 12 .006

Tanzania

The model was statistically significant for Tanzania with a statistical significance of 0.0001 at a 95% confidence level (0.0001 < 0.05)


Model summary


2 Std. error of the


country = Tanzania


1 .913a .833 .777 .69220 .833 14.963 4 12 .000


53

Uganda – Trade openness removed

The model was statistically significant for Uganda with a statistical significance of 0.0001 at a 95% confidence level (0.0001 < 0.05) with


Model summary


2 Std. error of the


country = Uganda R2 change F change df1 df2 Sig. F change

1 .951a .904 .881 .74761 .904 40.610 3 13 .000

Ghana – International reserves and financial integration removed

The model was statistically significant for Ghana with a statistical significance of 0.009 at a 95% confidence level (0.009 < 0.05) with


Model summary


2 Std. error of the


country = Ghana R2 change F change df1 df2 Sig. F change

1 .701a .492 .419 1.99326 .492 6.772 2 14 .009


54

5.4 Hypothesis testing

Chapter 3 described four hypotheses to be tested for resilience and vulnerability in

emerging economies using SSA countries to test the hypotheses. The discussion below

describes the results of the four tests that were conducted as well as the results from the

hypotheses.

5.4.1 Hypothesis 1

5.4.1.1 Testing of accumulation of international reserves

The first hypothesis tested whether the accumulation of international reserves of emerging

economies was a form of resilience against exogenous shocks. For this test, linear

regression was used to test the impact of international reserves as an independent

variable and the emerging economies’ GDP output as the dependent variable. Below are

the two hypotheses:


Accumulating international reserves or stock-piling is a useful means of building resilience

for emerging economies.


Accumulating international reserves or stock-piling is not a useful means of building


5.4.1.2 Hypothesis 1: Testing results

The results for hypothesis testing for international reserves or stock-piling as a useful

means of building resilience as the NULL hypothesis are presented on Table 5.1 for each

country. Refer to Appendix B for detailed results.


55

Table 5.1 Hypothesis 1: Summary

Country Statistical significance (α)

p-value Result

Liberia 0.05 0.984 NULL hypothesis rejected

Sierra Leone 0.05 0.966 NULL hypothesis rejected

Guinea 0.05 0.994 NULL hypothesis rejected

Namibia 0.05 0.22 NULL hypothesis rejected

Sudan 0.05 0.555 NULL hypothesis rejected

Uganda 0.05 0.00005 Failed to reject NULL hypothesis

Tanzania 0.05 0.068 NULL hypothesis rejected

Zambia 0.05 0.778 NULL hypothesis rejected

Nigeria 0.05 0.636 NULL hypothesis rejected

Morocco 0.05 0.907 NULL hypothesis rejected

Ethopia 0.05 0.988 NULL hypothesis rejected

Rwanda 0.05 0.278 NULL hypothesis rejected

South Africa 0.05 0.274 NULL hypothesis rejected

5.4.2 Hypothesis 2

5.4.2.1 Testing of economic concentration

The second hypothesis tested whether the reduction of the export economic concentration

of emerging economies was a form of resilience against exogenous shocks. For this test,

linear regression was used to test the impact of economic concentration as an

independent variable and the emerging economies’ GDP output as the dependent

variable. Below are the two hypotheses:


Reducing economic concentration is a useful means of building resilience for emerging

economies.


56


Reducing economic concentration is not a useful means of building resilience for emerging

economies.


The results for hypothesis testing for reducing export economic concentration as a useful

means of building resilience as the NULL hypothesis are presented in Table 5.2 for each

country. Refer to Appendix C for detailed results.


Country Statistical significance (α) p-value Result

Mauritius 0.05 0.021 Failed to reject NULL hypothesis




Mozambique 0.05 0.861 NULL hypothesis rejected

Ghana 0.05 0.759 NULL hypothesis rejected


Lesotho 0.05 0.861 NULL hypothesis rejected

Cape Verde 0.05 0.335 NULL hypothesis rejected

Sudan 0.05 0.143 NULL hypothesis rejected

Gambia 0.05 0.824 NULL hypothesis rejected

Uganda 0.05 0.149 NULL hypothesis rejected




Ethiopia 0.05 0.454 NULL hypothesis rejected

Rwanda 0.05 0.561 NULL hypothesis rejected

South Africa 0.05 0.638 NULL hypothesis rejected


57

5.4.3 Hypothesis 3

5.4.3.1 Testing of trade openness

The third hypothesis tested whether the degree of openness to trade in emerging

economies rendered the economy vulnerable to exogenous shocks. For this test, linear

regression was used to test the impact of openness of an economy as an independent

variable and the emerging economies’ GDP output as the dependent variable. Below are

the two hypotheses:


Trade openness to the global economy does not render emerging economies vulnerable to

exogenous shocks.


Trade openness to the global economy renders emerging economies vulnerable to

exogenous shocks.


The results for hypothesis testing for trade openness showed that this phenomenon does

not result in economic vulnerability as shown by the NULL hypotheses presented in Table

5.3 for each country. Refer to Appendix D for detailed results.


58



Mauritius 0.05 0.236 NULL hypothesis rejected




Mozambique 0.05 0.037 Failed to reject NULL hypothesis


Cape Verde 0.05 0.008 Failed to reject NULL hypothesis

Gambia 0.05 0.699 NULL hypothesis rejected

Sudan 0.05 0.027 Failed to reject NULL hypothesis




Ethiopia 0.05 0.84 NULL hypothesis rejected

5.4.4 Hypothesis 4

5.4.4.1 Testing of financial integration

The fourth hypothesis tested whether the level of integration of emerging economies

exposed these economies and rendered them vulnerable to exogenous shocks. For this

test, linear regression was used to test the impact of the integration level of an economy to

the global economy as an independent variable and the emerging economies’ GDP output

as the dependent variable. Below are the two hypotheses:


Financial integration in the global economy renders emerging economies vulnerable to

exogenous shocks.


59


Financial integration to the global economy does not render emerging economies



The results for hypothesis testing showed that the level of financial integration level has an

impact on emerging economies as the NULL hypotheses are presented in Table 5.4 for

each country. Refer to Appendix E for detailed results.



Mauritius 0.05 0.001 Failed to reject NULL hypothesis



Mozambique 0.05 0.053 Failed to reject NULL hypothesis

Ghana 0.05 0.007 Failed to reject NULL hypothesis

Lesotho 0.05 0 Failed to reject NULL hypothesis

Cape Verde 0.05 0.168 NULL hypothesis rejected

Sudan 0.05 0 Failed to reject NULL hypothesis

Uganda 0.05 0 Failed to reject NULL hypothesis

Tanzania 0.05 0.005 Failed to reject NULL hypothesis


Nigeria 0.05 0.015 Failed to reject NULL hypothesis

Morocco 0.05 0.562 NULL hypothesis rejected

5.5 Multicollinearity results

Multicollinearity tests were conducted and a number of economic parameters were found

to be highly correlated and were removed from the multiregression model. As indicated in

the methodology section in Chapter 4, an independent variable with an R2 of 0.9


60

introduced multicollinearity in the model and was therefore removed. Table 5.5 illustrates

all the independent variables that were removed due to multicollinearity for each country.

Refer to Appendix F for detailed results.

Table 5.5 Removed economic parameters

Country International reserves

Economic concentration

Trade openness

Financial integration

Mauritius X

Mozambique X

Ghana X X

Lesotho X X

Cape Verde X X

Namibia X

Gambia, The X X

Nigeria

Morocco X X

Rwanda X

South Africa X


61

5.6 Summary of hypotheses results

Table 5.6 summarises all results from the hypotheses tests that were performed.

Table 5.6 Summary of hypotheses results

Country Hypothesis 1 Reserves Hypothesis 2 Concentration Hypothesis 3 Openness Hypothesis 4 Integration

Mauritius Failed to reject NULL hypothesis NULL hypothesis rejected Failed to reject NULL hypothesis

Liberia NULL hypothesis rejected NULL hypothesis rejected NULL hypothesis rejected NULL hypothesis rejected

Sierra Leone NULL hypothesis rejected NULL hypothesis rejected NULL hypothesis rejected NULL hypothesis rejected

Guinea NULL hypothesis rejected NULL hypothesis rejected NULL hypothesis rejected Failed to reject NULL hypothesis

Mozambique NULL hypothesis rejected Failed to reject NULL hypothesis NULL hypothesis rejected

Ghana NULL hypothesis rejected Failed to reject NULL hypothesis

Namibia NULL hypothesis rejected NULL hypothesis rejected NULL hypothesis rejected

Lesotho NULL hypothesis rejected Failed to reject NULL hypothesis

Cape Verde NULL hypothesis rejected Failed to reject NULL hypothesis

Sudan NULL hypothesis rejected NULL hypothesis rejected Failed to reject NULL hypothesis Failed to reject NULL hypothesis

Uganda Failed to reject NULL hypothesis NULL hypothesis rejected NULL hypothesis rejected Failed to reject NULL hypothesis

Tanzania NULL hypothesis rejected NULL hypothesis rejected NULL hypothesis rejected Failed to reject NULL hypothesis

Zambia NULL hypothesis rejected NULL hypothesis rejected NULL hypothesis rejected NULL hypothesis rejected

Gambia, The NULL hypothesis rejected NULL hypothesis rejected

Nigeria NULL hypothesis rejected NULL hypothesis rejected NULL hypothesis rejected Failed to reject NULL hypothesis

Morocco NULL hypothesis rejected NULL hypothesis rejected

Ethiopia NULL hypothesis rejected NULL hypothesis rejected NULL hypothesis rejected NULL hypothesis rejected

Rwanda NULL hypothesis rejected NULL hypothesis rejected NULL hypothesis rejected

South Africa NULL hypothesis rejected NULL hypothesis rejected NULL hypothesis rejected


62

CHAPTER 6 Discussion of results

6.1 Introduction

Chapter 3 formulated hypotheses that were evaluated against a sample of 20 SSA

countries. The research methodology used for the evaluation of the hypotheses was

formulated in Chapter 4. The data results were presented in Chapter 5.

The aim of this chapter is to draw conclusions based on the results presented in Chapter 5

and also to gain a better understanding of these results. The overall objective of this

research study was to evaluate emerging economy resilience to and vulnerability against

adverse economic shocks through the use of four specific macroeconomic determinants

based on previous emerging economy research. These determinants were used and

applied to a sample of 20 SSA economies that attracted a significant amount of FDI in

relation to their size of economies.

The period under evaluation was from 1995 to 2011 (17 years). This was largely

determined by the availability of the data that were required to evaluate the stated

hypotheses in Chapter 3, specifically the concentration ratio that was compiled by the

UNCTAD based on the sector export concentration for SSA countries. This data collection

commenced in 1995.

6.2 Hypothesis 1 discussion: Accumulation of international reserves

The first hypothesis stated:

H1: Accumulating international reserves or stock-piling is a useful means of

building resilience for emerging economies.

The accumulation of international reserves has been observed in emerging economies

recently as a means of creating a barrier against exogenous shocks (Choi et al., 2009).

This trend has been noticed with regard to many emerging economies, more specifically

the Asian economies after the 1997 Asian financial crisis. Choi et al. (2009) noted that


63

relative to the size of their economies, these emerging economies had accumulated more

reserves than most developed economies. Demir (2009) attributed some reasons for the

stock-piling of international reserves to the volatility of international markets, mainly in the

prices of commodity goods which severely affected emerging economies. Emerging

economies are particularly vulnerable to these changes due to their dependence on

commodities which are susceptible to price and demand factors.

Choi et al. (2009) also attributed the international reserve accumulation to the emerging

economies not having access to capital at relatively low costs as compared to advanced

economies. Hence, the international reserve accumulation brings forth the critical issue of

access to capital access when required as a result of exogenous shocks being

experienced by these economies. However, it was advocated that emerging economies

resist stock-piling of international reserves and instead increase the amount of borrowing

by means of a shift to the economy’s tradable sector in order to relax borrowing constraints

(Korinek, 2011).

A sample of 20 SSA countries was evaluated for the hypothesis to test whether the

accumulation of international reserves has a statistically significant positive influence on

SSA economies’ economic performance. Six countries were removed from the analysis of

this parameter due to multicollinearity in the regression model. Thirteen of the evaluated

economies rejected the NULL hypothesis (p-value > 0.05) with only Uganda failing to

reject the NULL hypothesis. These results suggested that the accumulation of international

reserves did not provide a useful mechanism for the building of resiliency for SSA

economies.

6.2.1 Evaluation of economic performance

Time-series data analysis over the 17-year period (from 1995 until 2011) for international

reserves accumulation indicated that SSA economies accumulated minimal international

reserves and have been running current account deficits since 2007 as illustrated in Figure

6.1, showing mean average international reserves for each country. A time-series analysis


64

plotted on the same graph indicated a significant and increasing amount of international

reserves. There was a sharp decrease of these reserves during the 2008/09 financial

global crisis, which indicated that these reserves were tapped into as a form of resilience

mechanism. As illustrated in Figure 6.1, SSA economies did not have international

reserves to dampen the impact of the 2008/09 financial global crisis compared with the

Asian Tigers which successfully utilised reserves as a barrier against sudden shortages of

capital (Choi et al., 2009). This would explain the insignificance of international reserves as

a form of resilience against exogenous shocks for SSA economies.

Figure 6.1 Hypothesis 1: Mean average current account balance

Even though international reserve accumulation was statistically insignificant for SSA

economies, spurious positive standardised coefficients mean average of 0.23 indicated

that international reserves do not have a significant influence on the dependent variable,

the economic growth. Rather, Figure 6.2 indicated that SSA economies relied heavily on

increasing their debt stock to cushion the impact of the 2008/09 global financial crisis even

in the aftermath of the crisis.


65

Figure 6.2 Hypothesis 1: Mean average external debt stock

6.2.2 Summary of hypothesis findings

From the analysis, it is evident that the concept of utilising international reserves as a

resilience factor against exogenous shocks was found not to be applicable. This suggests

that SSA economies did not build up sufficient international reserves when compared with

other emerging economies such as the Asian Tigers as demonstrated in Figure 6.1. Choi

et al. (2009) suggested that emerging economies build up international reserves on which

they could draw in the event of an exogenous shock. The research concluded that the

international reserves accumulation by the Asian economies was due to the economies

not being able to access funding like the developed economies. However, this conclusion

was not substantiated for the SSA economies in that they did not use this measure as a

form of resilience. Korinek (2011) suggestion that emerging economies decrease the

amount of international reserves in favour of borrowing was evidenced by these

economies.


66

6.3 Hypothesis 2 discussion: Economic concentration

The second hypothesis stated:

H2: Reducing economic concentration is a useful means of building economic


It has been argued that economic diversification in terms of exports from an emerging

economy is a form of resilience and protects an emerging economy from exogenous

shocks (Hosein, 2010). This is largely due to the heavy reliance of emerging economies on

minerals and natural resources (Eichengreen, 2002). Exogenous shocks are exerted in

two forms on these emerging economies, the first being the sudden changes in prices of

these commodities and the second being the demand factors for these commodities which

affect the income normally generated from minerals and other natural resources. Reducing

the economic concentration of an emerging economy principally means that should one

commodity or service suffer either of the above-mentioned shocks, this would not

necessarily devastate economic growth but would be minimised through the diversification

process. Empirical studies have developed recommendations for economic export

diversification as a driver of economic stabilisation which builds economic resilience

(Papageorgiou & Spatafora, 2012). In similar studies, there were, however, opposing

insights that argued that reducing economic concentration would essentially mean that the

emerging economy forfeits its economic competitive advantage (Mayer, 2006).

A sample of 20 SSA economies was evaluated. Morocco was excluded from this

hypothesis evaluation since this country did not have economic concentration data. The

hypothesis evaluated whether the reduction of economic concentration had any resilience

or significantly positive impacts on the countries’ economic growth. The hypothesis was

rejected for 18 (p-value < 0.05) of the 19 economies, with only Mauritius failing to reject the

hypothesis (p-value > 0.05). This suggested that reducing economic concentration or

increasing economic diversification had no significant impact on the resilience of SSA

economies.


67


Economic performance from 1995 to 2011 for all SSA economies that rejected and failed

to reject the NULL hypothesis (that reducing economic concentration is a useful means of

building economic resilience) was plotted on a time-series graph, as shown in Figure 6.3.

Figure 6.3 Hypothesis 2: Mean average exports data

From the mean export data for countries grouped according to their results for Hypothesis

2, it was evident that exports for countries that rejected the NULL hypothesis are more

vulnerable to global economic changes as can be seen with the impact of the global

financial crisis in 2008/2009. The country that failed to reject the NULL hypothesis

experienced a minor drop in export figures, suggesting that lower economic concentration

was effectively used as a mechanism to increase economic resilience against the

exogenous shock of the 2008/2009 global financial crisis. The economic concentration

data also supported this finding as the economic concentration ratio for economies that

were statistically significant and that failed to reject the NULL hypothesis was significantly


68

lower (mean concentration ratio = 0.26) compared to economies that were not statistically

significant and that rejected the NULL hypothesis (mean concentration ratio = 0.362). This

indicated that the lower the economic concentration was, the less impact exogenous

shocks would have on exports.

When evaluating the standardised coefficient, Freud and Littell (2000) stated that it is

“useful in ascertaining the relative importance of the effects of independent variables not

affected by the scales of measurement” (p. 29). For the countries that failed to reject the

NULL hypothesis, economic concentration was the second most influential factor on the

independent variable (GDP economic growth) with a positive 0.636 standardised

coefficient. This implied that the lower concentration ratio had had a positive change on

the GDP economic growth.

Observing the mean export figures for the two groups of economies in Figure 6.4, it was

apparent that less economic concentration for SSA economies resulted in less export

growth. The economies where the concentration ratio was not significant experienced an

exponentially higher export growth. The exponential export growth also resulted in a much

higher mean GDP growth. Figure 6.4 also strengthened the argument that a lower

concentration ratio insulated the economies from the 2008/2009 financial global crisis.


69

Figure 6.4 Hypothesis 2: Mean average GDP

Observing the mean export figures for the two groups of economies in Figure 6.3, it was

apparent that less economic concentration for SSA economies resulted in less export

growth. Economies where the concentration ratio was not significant experienced an

exponentially higher export growth. The exponential export growth also resulted in much

higher mean GDP growth (figure 6.4). Figure 6.4 also strengthened the argument that a

lower concentration ratio insulated the economies from the 2008/2009 financial global

crisis.


The concentration ratio was not an effective means of building resilience in most of the

SSA economies. There were significant differences in economic performance between

economies where the concentration ratio played a positive role in the economic growth of

an economy and those where the concentration ratio displayed no significant impact.

Hosein (2010) argued that less economic concentration ensures economic stabilisation


70

and increased economic resilience for emerging economies. This was observed with the

data analysis performed in this study. These economies displayed economic resilience

where an exogenous shock had a minimal adverse impact. However, the data also

demonstrated that less economic concentration resulted in the slow growth in exports and

GDP which supported the research by Mayer (2006) that competitive advantage for

emerging economies is forfeited through decreased economic concentration. This

argument could provide a probable explanation in the observed slow growth in exports for

low economic concentration economies.

6.4 Hypothesis 3 Discussion: Trade openness

The third hypothesis stated:

H3: Trade openness to the global economy does not render emerging economies


There have been various arguments relating to the impact of openness relative to

macroeconomic performance when exogenous shocks are exerted. There are two schools

of thoughts on the subject of the degree of openness of an economy, The first is that of

Duval et al. (2007) who, in their research on this topic, concluded that the degree of

openness of an economy enhances the resilience of the economy and reduces

vulnerability to exogenous shocks, citing the Singaporean economy in what was termed

the Singapore Paradox. They argued that the high degree of openness essentially

contributed to the high resilience experienced by this economy. This finding disputed the

outcomes of research conducted on emerging economies by Eichengreen (2002). This

research concluded that openness rendered emerging economies vulnerable since these

economies are heavily reliant on commodities. Eichengreen (2002) argued that these

exogenous shocks in the form of demand and price changes could easily be transmitted to

emerging economies with severe consequences.


71

A sample of 20 SSA economies was evaluated for this hypothesis. Gambia was excluded

from the evaluation since the country did not have import and export data to compute the

trade openness input variable. The hypothesis aimed to evaluate whether trade openness

had a significant impact on the economic output of the SSA economies. The NULL

hypothesis was rejected (p-value > 0.05) for 11 of the 19 economies with the remainder of

economies removed due to multicollinearity. This suggested that the degree of trade

openness did render most SSA economies vulnerable to adverse economic shocks.


The NULL hypothesis that trade openness does not render SSA economies vulnerable to

exogenous shocks, using economic data over a 17-year period, was rejected for 11 of the

19 economies. The economies that failed to reject the NULL hypothesis were separated

from the economies that rejected the NULL hypothesis. Average means were computed to

evaluate these economies against adverse economic shocks to track and extrapolate

findings from their economic performance. Figure 6.5 demonstrates the differences in

terms of GDP growth between these economies, particularly during the two exogenous

shocks of the twenty-first century (the 2001/2002 and 2008/2009 economic crises).

Trade liberalisation has often been advocated by the World Bank and the IMF as a major

source of economic growth via the Washington Consensus which promotes a pro-

globalisation approach (Cavallo & Frankel, 2007) as it encourages economic activity and

competition (Yahikkaya, 2002). The view advocated by the IMF is that trade openness is

associated with economic growth. In the case of SSA economies, it was clear that those

economies that were not vulnerable experienced higher economic growth. Furthermore,

from the research results, economies that rejected the NULL hypothesis, in other words,

economies that were rendered vulnerable to exogenous shocks (which in this case were

11 of the 19 economies) were badly affected by the two exogenous shocks and economic

growth was volatile with a boom-bust cycle while also experiencing lower economic

growth, as illustrated in Figure 6.5. Yet, the statically significant economies experienced


72

higher and more stable economic growth with no notable impact as a result of the global

financial crisis.


Yahikkaya (2002) stated that economies with a higher share of trade experience higher

economic growth. In this study, trade volumes in the form of exports traded by SSA

economies that were vulnerable to exogenous shocks and trade volumes are plotted in

Figure 6.6.This graph illustrates that economies that were vulnerable to exogenous shocks

traded or exported higher volumes than economies that were statistically significant and

that were not rendered vulnerable to exogenous shocks. It was also evident that these

economies tended to have a much better rate of recovery in exports once the exogenous

shock had ceased. Cavallo and Frankel (2007) offered a plausible explanation for this

recovery, namely that more openness may reduce the cost of the trade deficit and

encourage FDI to facilitate more commercial trade in the developing economies.


73

Figure 6.6 Hypothesis 3: Mean average exports

Indeed, the observations by Cavallo and Frankel (2007) that openness tends to encourage

FDI in economies especially post-crisis was the case in those SSA economies that were

more open to trade and rendered more vulnerable to exogenous shocks. This can be seen

in Figure 6.7 in the trade deficit experienced by these economies in the aftermath of the

global financial crisis.


74

Figure 6.7 Hypothesis 3: Mean average current account balance

The trade openness standardised coeifficients affected the three statistically significant

economies (Mozambique, Cape Verde and Sudan) differently. Since standardised

coefficients measure the strength of the impact of the independent variable on the

dependent variable in absolute units (Freud and Littell, 2000), this measure was used to

assess the impact of trade openness on these economies. For Mozambique, trade

openness had a standardised coefficient of –0.459, implying that trade openness heavily

influenced Mozambique’s economic performance. However, the minus sign (–) meant that

trade openness had a fundamentally adverse impact on economic performance. This was

a similar finding for Cape Verde with a standardised coefficient of –0.632. For Sudan,

trade openness had a standardised coefficient of 0.69, which signified that trade openness

had a significant positive impact on Sudan’s economic performance.


75


Trade openness rendered most SSA economies vulnerable to exogenous shocks as the

NULL hypothesis (that trade openness does not render SSA economies vulnerable to

exogenous shocks) was rejected for 11 of the 19 economies. Further, the economies were

separated into two groups based on their statistical significance to compare and assess

the impact of the global financial crisis on these economies. This offered deeper insights

into these two types of economies. Evidence from the data analysis implied that the

economies that were vulnerable due to trade openness to the global economy tended to

export more than the economies that were not vulnerable due to trade openness. This

signified greater economic growth. Prasad, Rogoff, Wei and Kose (2006) confirmed that

trade openness contributes greatly to promote economic growth, a phenomenon which

was observed in the time-series analysis.

Economic growth volatility was also evident for economies that were vulnerable due to

trade openness. This alluded to the finding that trade openness often resulted in output

volatility for developing economies. This finding was supported by Prasad et al. (2003)

who argued that an increase in trade openness leads to higher output volatility for

developing countries. This is another factor of vulnerability to adverse shocks (Di Giovanni

& Levchenko, 2007). Data analysis also illustrated that economies that were vulnerable

due to trade openness had greater external assets. During the global financial crisis, these

economies accumulated financial deficits which suggested that they were accessing their

financial assets and borrowing with the current account deficit increasing significantly after

2010. This signalled global economic recovery, resulting in greater FDI in these

economies, and was consistent with the hypothesised positive relationship between trade

openness and FDI (Asiedu, 2002).


76

6.5 Hypothesis 4 Discussion: Financial integration

The fourth hypothesis stated:

H4: Financial integration in the global economy renders emerging economies


Kose et al. (2006) argued that financial integration is essential for emerging economies to

increase economic growth as it provides emerging economies with access to capital

markets. Indeed, access to capital markets is critical for emerging economies since they

rely heavily on FDI to grow their economies. However, other findings indicated that

financial integration is often beneficial for middle-income and wealthy countries rather than

for developing economies (Mougani, 2012). Emerging economies, such as those in SSA,

are heavily reliant on commodities which are adversely affected by demand and price

changes with boom-bust cycles transmitted through financial integration (Korinek, 2011).

Kose et al. (2006) observed that emerging economies that are financially integrated in the

global economy experience high growth but also experience significant volatility and are

thus vulnerable to transmitted economic shocks.

Initially, a sample of 20 countries was used to evaluate the fourth hypothesis. From the

collected data, three countries (Mozambique, Namibia and Gambia) were removed from

the hypothesis analysis due to missing data. Seven countries (Mauritius, Guinea, Ghana,

Sudan, Uganda, Tanzania and Nigeria) failed to reject the NULL hypothesis (p < 0.05)

(whether financial integration renders emerging economies vulnerable to exogenous

shocks) since it was statistically significant. Therefore, there was no outright consensus for

this hypothesis as almost the same number of countries rejected the NULL hypothesis as

those that failed to reject the NULL hypothesis.


77


The hypothesis was statistically significant (p-value < 0.05) for seven SSA economies

which failed to reject the NULL hypothesis. These economies were Mauritius, Guinea,

Ghana, Sudan, Uganda, Tanzania and Nigeria. The statistically significant economies

were observed using time-series data over a 17-year period (1995 to 2011) to discern the

phenomena that have been associated with financial integration of emerging economies

with the SSA economies as performance measures. The economies that were statistically

significant displayed lower economic growth than the economies that were not rendered

vulnerable by financial integration with the global economy, as illustrated in Figure 6.8, by

the mean growth rate year-on-year.

These results for statistically significant SSA economies were not consistent with Korinek

(2011) who argued that emerging economies are vulnerable to boom-bust cycles and

volatility in their economic growth. However, for SSA economies that rejected the NULL

hypothesis, this finding was consistent with Kose et al. (2006) in that these economies

experienced high levels of economic growth with high volatility. Figure 6.8 also indicates

that the 2008/09 global financial crisis affected both sets of economies equally, with the

economies that were vulnerable to exogenous shocks displaying a faster recovery rate

after the crisis.


78

Figure 6.8 Hypothesis 4: Mean average GDP growth

Mougani (2012) stated that financial integration provides emerging economies with access

to capital markets, and this enables economies with capital allocation across capital

markets. SSA economies that were statistically significant (p-value < 0.05) for the NULL

hypothesis, and hence rendered vulnerable to exogenous shocks due to their financial

integration in the global economy, illustrated higher external liabilities pre-2004. This is

seen in Figure 6.9 showing the mean external debt stocks for statistically significant

economies.

The observation by Mougani (2012) for financially integrated economies was

demonstrated by the sharp decrease in external debt stocks, as illustrated in Figure 6.9.

According to The World Bank (2005), a Multilateral Debt Review Relief Initiative was

undertaken at a G8 summit meeting in Gleneagles, Scotland to cancel the debt of heavily

indebted developing countries of which many of the SSA countries were recipients. This

had a significant impact for the statistically significant economies in that the average mean

external debt stocks decreased from US$12 billion in 2004 to under US$6 billion. This


79

occurrence displayed one of the advantages that emerging economies enjoy as a result of

their financial integration in the global economy even though these economies are

vulnerable to global economic shocks as well.

Figure 6.9 Hypothesis 4: Mean average external debt stocks

Reviewed literature on the subject of financial integration of emerging economies, such as

Korinek (2011), Mougani (2012) and Kose et al. (2006), explored the impact of financial

integration on emerging economies’ economic growth, increased volatility and impact on

FDI. However, in this study based on the SSA region, a critical observation was

discovered based on the economies that were rendered vulnerable (statistically significant

p-value) to exogenous shocks due to their financial integration in the global economy.

Figure 6.9 illustrates the inflection point in 2005 when debt relief was granted to heavily

indebted developing economies by the G8. This point was the same inflection point in

2005 when, according to Figure 6.9, these economies surpassed the mean average GDP

of SSA economies that were not vulnerable to exogenous shocks (statistically insignificant

p-value). This illustrated causation, bearing in mind the influence of other economic

factors, as decreasing these economies’ debt stocks led to significant increases in the size


80

of these economies. Results illustrated on a time-series analysis in Figure 6.10 were also

consistent with Korinek (2011) who argued that emerging economies that are financially

integrated in the global economy are exposed to global economic cyclical changes. It was

evident from Figure 6.10 that the 2008/09 financial global crisis caused a contraction in the

sampled SSA economies.


The SSA economies that were found to be vulnerable to exogenous shocks due to their

financial integration were able to accumulate more international reserves, as shown in

Figure 6.11, than the economies that were not vulnerable to exogenous shocks or

statistically not significant. This finding was consistent with that of Kose et al. (2006) that

these economies, even though they were vulnerable to economic shocks, had more

access to capital markets.


81

Figure 6.11 Hypothesis 4: Mean average total reserves


Almost the same number of SSA economies was vulnerable due to financial integration as

those that were not. Numerous aspects of these economies were explored to evaluate the

conventional emerging economics theories against those of the SSA region. Korinek

(2011) purported that emerging economies are exposed to boom-bust cycles owing to their

financial integration. From the data analysis, this finding was detected although exposure

was to a lesser extent when compared to SSA economies that were not seen as

vulnerable to exogenous shocks due to financial integration in the global economy.

However, findings were consistent with Korinek (2011) on the exposure of the statistically

significant emerging economies to the 2008/09 global financial crisis. Kose et al. (2006)

noted that emerging financially integrated economies experienced higher economic growth

rates.


82

This research concurred with the finding that statistically significant SSA economies

experienced higher GDP growth rates although the eternal debt stocks were much lower.

This finding was also influenced by the critical decision by the G8 in 2005 to provide debt

relief to many of the SSA economies, providing a turning point for the economies that had

a statistically significant p-value for the hypothesis.

6.6 Summary of results

The analysis of the four hypotheses contributed to understanding the dynamics between

the four selected economic factors that contribute to strengthening economic vulnerability

and resilience of SSA economies. These factors were assessed with economic growth in

mind as they have proved to be essential ingredients to attract FDI to the region according

to the literature review. In the first hypothesis, SSA countries, contrary to the Asian

economies, did not effectively utilise international reserves as a means of enhancing their

resilience but instead relied heavily on borrowings through accumulation of external debt

stocks. The Asian economies were able to accumulate international reserves while SSA

economies were incurring current account deficits which would indicate investment in

infrastructure or large amounts of FDI inflow.

The second hypothesis discovered that economic concentration was also not a useful

means of enhancing resilience for SSA economies. Data analysis indicated that SSA

economies were rendered vulnerable to demand changes in commodities and were

severely affected by the global financial crisis. However, the low economic diversification

allowed these economies to build competitive advantage in the products or commodities

they exported with the exports fuelling rapid economic growth. The data suggested that

enhancing resilience in the case of SSA economies essentially meant that these

economies could not harness competitive advantage to ensure high GDP growth.

Mauritius as the economy that was economically diversified achieved lower GDP growth,

but managed to attract the highest amount of FDI relative to its GDP. This highlighted that

investors were willing to investment in more economically stable SSA economies.


83

Trade openness was the third hypothesis that was assessed and indications were also

that SSA economies were rendered vulnerable to exogenous shocks and were also

severely affected by the financial global crisis as a result. Trade openness also meant

lower growth rates for SSA economies, even though these economies managed to

achieve high exports and build up current account surpluses. However, even these current

account surpluses were quickly eroded during the global financial crisis while current

account deficits increased beyond the global economic recovery, suggesting that trade

openness allowed these economies to access their financial assets.

Financial integration, the fourth hypothesis, provided mixed results with integration

rendering the same number of SSA economies vulnerable to exogenous shocks as those

that rejected this hypothesis. The economies that were rendered vulnerable to exogenous

shocks were those that had significant FDI relative to their GDPs, signalling that

investment capital could be easily withdrawn during a financial crisis, as was evident in the

data analysed. These economies also experienced higher volatility in their GDP growth as

a result. The economies that were not rendered vulnerable to exogenous shocks as a

result of financial integration were not affected by the global financial crisis. However, they

could also not benefit from financial relief efforts by global financial bodies such as the

World Bank and the IMF.

Overall, through the methodology undertaken to study the vulnerability and resilience of

SSA economies, the model was significant and applicable across seven of the economies.


84

CHAPTER 7 Conclusion

7.1 Summary of main findings

The increased importance of the SSA region in the context of FDI flow in the global

economy, more so in the aftermath of the global financial global crisis, has been

highlighted to be of paramount importance. The literature review suggested that investors

do not only seek high returns but also secure, stable returns on their investments when

investing in emerging or developing economies. Resilience and vulnerability concepts

emerged as topics of interest in improving SSA economies’ stability since the majority of

these economies have been achieving high economic growth outputs. However, the global

financial crisis eroded hard-fought-for gains in the growth of these economies. The

literature review also revealed that emerging economies still rely heavily on commodities

to grow their economies. This is a concern for the finance ministers of these economies

with formal agendas in the recently held World Economic Forum formulating strategies to

enhance dynamic resilience for developing economies.

Possible predictors that would have a direct impact on vulnerability and resilience of

emerging economies were selected as hypotheses. Most of the predictors were

extrapolated from Asian economies where extensive research had been undertaken. The

four constructs centred on the accumulation of international reserves, economic

diversification, openness to trade and integration of the financial sector in the global

economy. These predictors were of interest to emerging economies primarily because the

global financial crisis was as a result of a failure in the the financial system of developed

economies which had spillover effects for emerging or developing economies.

The main finding was that SSA economies were vulnerable to exogenous shocks and

were volatile even though they had managed to achieve high economic growth rates.

Unlike the Asian economies that had accumulated large amounts of international reserves

after the Asian crisis as a resilience mechanism, SSA economies did not accumulate these


85

external assets but instead relied on external debt to cope with the recent economic shock.

The finding on the non-accumulation of international reserves was that it did not render

SSA economies vulnerable to exogenous shocks. This was supported by the financial

integration of these economies in the global financial system in that the SSA economies

were able to access credit lines and increased their external debt stocks.

In light of the heavy reliance of emerging or developing economies on commodities, with

SSA economies being no exception, most research studies advocated economic

diversification as a form of resilience since commodities are highly volatile due to price and

demand changes. Fewer research studies suggested maintaining competitive advantage

through less economic diversification. The finding in the case of the SSA region was that

SSA economies were highly concentrated and resilience was compromised as a result.

However, the high economic concentration allowed these economies to increase

exponentially their exports due to the competitive advantage that they had managed to

establish. Volatility in exports and economic growth was also found to be very high. The

few economies that managed to diversify, such as Mauritius, achieved very low exports

along with correspondingly low economic growth output. However, this economy

reinforced the notion that investors are indeed looking for stable emerging or developing

markets as the economy attracted the largest FDI in the SSA region.

Most SSA economies were highly open to trade and, as a result, were highly exposed to

exogenous shocks. This, together with the high economic concentration factor, rendered

most SSA economies vulnerable. The few economies that were not exposed to exogenous

shocks were found to be protectionist economies based on the low trade export volumes

and GDP growth was thus stable with no signs of economic recession during the financial

global crisis. Financial integration presented mixed results in the evaluation of vulnerability

due to SSA economies’ integration in the financial global economy. The results were not

conclusive for financial integration as economies that attracted significant amounts of FDI

were rendered vulnerable to exogenous shocks. Although negatively affected by the global

financial crisis, the recovery in economic growth output was instantaneous, signalling

investors were willing to re-invest in the economies. Financial integration also allowed SSA


86

economies to access borrowings in the form of external debt stocks, which compensated

the lack of international reserves.

7.2 Research recommendations

Results from Chapter 6 provided insight into resilience and vulnerability levers for SSA

economies. An application model, illustrated in Figure 7.1 and based on the results from

Chapter 6, is recommended for managers and investors who could use the model to

assess the drivers of resilience and vulnerability on which investment decisions can be

based. The model can also be used as a guideline for monetary and fiscal policy

formulation for SSA countries and as a predictor based on the four economic pillars under

evaluation.

Figure 7.1 Resilience and vulnerability model for sub-Saharan African economies


87

The model combines all the concepts under evaluation and their overall impact on the

stability and performance of SSA economies. The model assumes that vulnerability can

either be reduced or can expose the economy to exogenous shocks, while resilience can

either be enhanced or compromised.

For vulnerability, trade openness and financial integration are viewed as drivers. According

to the model, an increase in trade openness results in an increase in vulnerability with mild

volatile economic growth. A decrease in trade openness with the global economy

decreases the economy’s vulnerability with stable, relatively high economic growth.

The model suggests that an increase in financial integration in the global economy

introduces more vulnerability to exogenous shocks with volatile high economic growth as a

consequence. A decrease in financial integration reduces vulnerability and exposure to

exogenous economic shocks with an output of low but stable economic growth.

For resilience, the model uses international reserves and economic concentration as

levers. Data evaluated for SSA economies suggests that increasing international reserves

compromises and reduces the economy’s resilience to exogenous shocks with low volatile

economic growth output. A decrease in international assets or reserves enhances the

economy’s resilience against shocks with stable high economic growth output.

The second driver of resilience is economic concentration. An increase in economic

concentration (in other words, an over-reliance on main products, services or

commodities) reduces the economy’s resilience to exogenous shocks and results in high

but volatile economic growth. A decrease in economic concentration (diversifying the

country’s exports) enhances resilience against exogenous economic shocks and results in

mild but stable economic growth output.

The model also provides recommendations for SSA policy makers by bringing all the

pillars together to propose high-level actions to produce stable, mild economic growth by

reducing vulnerability while enhancing economic resilience by reducing trade openness,


88

reducing financial integration, reducing international reserves and reducing economic

concentration. On the other end of the spectrum, to produce high economic growth output,

policy makers can (as the model suggests) increase trade openness, increase financial

integration and increase economic concentration for competitive advantage. However, with

this approach, the ensuing result would be reduced resilience and increased vulnerability

to exogenous shocks.

7.3 Research limitations

Due to the limited scope of the study, limitations to the study were identified and are listed

below:

The study focused on emerging economies and the SSA region was used as a

case in point. Hence, the findings may not be applicable to other regions or

developing economies as they may have inherently different structures to the

economies assessed in this study.

The selected methodology used to evaluate the economies was a multiregression

analysis instead of regression analysis to assess each country on an individual

basis so as to eliminate any possibility of multicollinearity.

It is possible that there are many more economic parameters that may render

economies vulnerable or resilient apart from the four that were selected for this

assessment. The model was developed based on the results of these economic

parameters. As such, the model may not be entirely accurate as there are many

other factors that may influence or determine economic vulnerability and resilience.

The results of regression analysis may not prove causality but may determine

correlation between the input variables.


89

A number of economic evaluations for the stated hypotheses were not statistically

significant. Therefore, had more economies been included in the study, then

different results might have been obtained.

7.4 Recommendations for future research

Limitations of this research study were identified above. The following suggestions could

be implemented in future research to add to the body of research in this area:

Expand the number of economies in the study to increase the statistical

significance and accuracy of the results.

Select more variables that were considered in this research study and compare the

results with the results obtained in this study.

Emerging or developing economies from different continents could be selected to

reduce the likelihood of studying homogeneous economies and to eliminate strong

characteristics associated with the SSA economic region so that the results are

applicable across a wider context.

A different research methodology could be selected to improve the reliability and

validity of results, and to eliminate auto-correlation that is associated with

evaluating numerous independent variables when using regression analysis.

7.5 Conclusion

The aim of this study was to evaluate and assess the economic vulnerability and resilience

of emerging economies to adverse exogenous shocks using the SSA region as a case in

point. The outcomes of this research have expanded the body of research on the concepts


90

of vulnerability and resilience for the SSA region as most of the previous research had

focused on emerging or developing economies in Asia and Latin America.

The model developed from this research provides the basis for a decision-making

framework for macroeconomic policy formulation which can later be expanded through

future research in this area. Analysis using the methodology followed has added to

existing literature in the area of developing strategies for developing economies to be

better prepared for unknown future adverse events and to minimise the loss of hard-

fought-for economic gains.


91

Reference list

Adewumi, S. (2006). The impact of FDI on growth in developing countries: An African

experience. (Master’s thesis, Jönköping International Business School). Retrieved

from http://hj.diva-portal.org/smash/get/diva2:4440/FULLTEXT01

Aryeetey, E., & Ackah, C. (2011). The global financial crisis and African economies:

Impact and transmission channels. African Development Review, 23(4), 407–420.

doi:10.1111/j.1467-8268.2011.00295.x

Asiedu, E. (2006). Foreign direct investment in Africa: The roles of natural resources,

market size, government policy, institutions and political instability. World

Economy, 29(1), 63–77. doi:10.1111/j.1467-9701.2006.00758.x

Bluen, S. (2012). Talent management in emerging markets. Johannesburg: Knowres

Publishing.

Briguglio, L. (2003). The Vulnerability Index and small island developing states: A review

of conceptual and methodological issues. University of Malta. Retrieved from

http://www.um.edu.mt/__data/assets/pdf_file/0019/44137/vulnerability_paper_sep0

3.pdf

Briguglio, L., Cordina, G., Farrugia, N., & Vella, S. (2009). Economic vulnerability and

resilience: Concepts and measurements. Oxford Development Studies, 37(3), 229–

247. doi:10.1080/13600810903089893

Calkins, S. (2002). The new merger guidelines and the Herfindahl-Hirschman Index,

California Law Review, 71(2), 402–429. Retrieved from

http://scholarship.law.berkeley.edu/cgi/viewcontent.cgi?article=2149&context=califo

rnialawreview


92

Cavallo, E. A. & Frankel, J. A. (2007). Does openness to trade make countries more

vulnerable to sudden stops, or less? Using gravity to establish. Econstor Working

Paper. Inter-American Development Bank. Retrieved from

http://www.econstor.eu/handle/10419/51509

Cheung, Y. & Ito, H. (2008). Hoarding of international reserves: A comparison of the Asian

and Latin American experiences. HKIMR Working Paper No.7/2008. Hong Kong

Institute of Monetary Research. Retrieved from

http://www.hkimr.org/uploads/publication/166/ub_full_0_2_176_hkimr-working-

paper-no-7_bw.pdf

Choi, W. G., Sharma, S. & Strömqvist, M. (2009). Net capital flows, financial integration,

and international reserve holdings: The recent experience of emerging markets and

advanced economies. International Monetary Fund Staff Papers,56, 516–540.

doi:10.1057/imfsp.2008.35

Christopherson, S., Michie, J., & Tyler, P. (2010). Regional resilience: Theoretical and

empirical perspectives. Cambridge Journal of Regions, Economy and Society, 3(1),

3–10. doi:10.1093/cjres/rsq004

Demir, F. (2009). Volatility of short term capital flows, financial anarchy and private

investment in emerging markets. Journal of Development Studies, 45(5), 672–692.

Retrieved from http://faculty-staff.ou.edu/D/Firat.Demir-

1/Demir_JDS09_Volatility_of_Capital_Flows_and_Private_Investment.pdf

Devarajan, S., & Kasekende, L. A. (2011). Africa and the global economic crisis: Impacts,

policy responses and political economy. African Development Review, 23(4), 421–

438. doi:10.1111/j.1467-8268.2011.00296.x


93

Di Giovanni, J. & Levchenko, A. A. (2007). Trade openness and volatility. International

Monetary Fund. Retrieved from

http://www.imf.org/external/pubs/ft/wp/2008/wp08146.pdf

Donnelly, L. (2013, May 10). Gordhan warns of Africa’s ‘vulnerability’ despite economic

growth. Mail & Guardian. Retrieved from http://mg.co.za/article/2013-05-10-

gordhan-warns-of-africas-vulnerability-despite-economic-growth

Dos Santos, F. T., & Partidário, M. R. (2011). SPARK: Strategic planning approach for

resilience keeping. European Planning Studies, 19(8), 1517–1536.

doi:10.1080/09654313.2011.533515

Down, I. (2007). Trade openness, country size and economic volatility: The compensation

hypothesis revisited. Business and Politics, 9(2), 1–22. Retrieved from

http://www.unc.edu/depts/europe/conferences/tgsw/iandown-trade_openness.pdf

Duval, R. D., Elmeskov, J., & Vogel, L. (2007, July). Structural policies and economic

resilience to shocks. OECD Economics Department Working Paper No. 567.

Organisation for Economic Co-operation and Development. Retrieved from

http://search.oecd.org/officialdocuments/displaydocumentpdf/?doclanguage=en&co

te=eco/wkp(2007)27

Eichengreen, B. (2002, February). Can emerging markets float? Should they inflation

target? Banco Central Do Brasil, Working Paper Series 36, 1–49. Retrieved from

http://www.bcb.gov.br/pec/wps/ingl/wps36.pdf

Ernst & Young. (2013, May 6) Press release: Africa’s share of global FDI increases over

the last five years. Retrieved from http://www.ey.com/GL/en/Newsroom/News-

releases/News_Africa-share-of-global-FDI-increases-over-the-last-five-years

Ethington, B. (2005). Multicollinearity. Memphis: University of Memphis.


94

Fitch. (2013, May 22). Sub-Saharan Africa has gradual upgrade potential. Reuters.

Retrieved from http://www.reuters.com/article/2013/05/22/fitch-sub-saharan-africa-

has-gradual-upg-idUSFit65679220130522

Freund, R. J., & Littell, R. C. (2000). SAS system for regression. Cary, NC: SAS Institute.

Gerschewski, S. (2013). Do local firms benefit from foreign direct investment? An analysis

of spillover effects in developing countries. Asian Social Science, 9(4), 67–76.

doi:10.5539/ass.v9n4p67

Gnangnon, S. K. (2012). Does structural economic vulnerability matter for public

indebtedness in developing countries. CERDI, Etudes et Documents, n° 36,

September 2012. Retrieved from http://halshs.archives-

ouvertes.fr/docs/00/74/94/69/PDF/2012.36.pdf

Grewal, R., Cote, J. A. & Baumgartner, H. (2004) Multicollinearity and measurement error

in structural equation models: Implications for theory testing. Marketing Science, 23

(4), 519–529. doi: 10.1287/mksc.1040.0070

Guillaumont, P. (2010). Assessing the economic vulnerability of small island developing

states and the least developed countries. Journal of Development Studies, 46(5),

828–854. doi: 10.1080/00220381003623814

Harttgen, K., & Günther, I. (2006, October). Households' vulnerability to covariate and

idiosyncratic shocks. Proceedings of the German Development Economics

Conference. Conference conducted at the meeting of Verein für Socialpolitik,

Research Committee Development Economics, Berlin.


95

Hasan, H. & Banna, S. (2010). The unit of analysis in IS theory: The case for activity.

University of Wollongong, Faculty of Commerce – Papers. Retrieved from

http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1824&context=commpapers

Hoddinott, J., & Quisumbing, A. R. (2008, May). Methods for microeconometric risk and

vulnerability assessments. Washington, DC: International Food Policy Research

Institute. Retrieved from

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1281055

Hosein, R. (2010). Booming mineral resources and the imperative of economic

diversification. The West Indian Journal of Engineering, 32(1,2), 76–84. Retrieved

from http://sta.uwi.edu/eng/wije/vol3201-

02_jan2010/documents/EconomicDiversification.pdf

Hoskisson, R. E., Eden, L., Lau, C. M., & Wright, M. (2000) Strategy in emerging

economies. Academy of Management Journal, 43(3), 249–267. Retrieved from

http://www.voxprofessori.com/eden/Publications/strategyinemergingeconomies.pdf

Jayaraman, T. K. (2006). Macroeconomic reform and resilience building in small states. In

E. J. Kisanga, G. Cordina, & L. Brigugulio (Eds.), Building the economic resilience

of small states (pp. 33–38). Blata I-Bajda: Formatek.

Khanna, T., & Palepu, K. G. (2011). Winning in emerging markets: Spotting and

responding to institutional voids. World Financial Review, May–June, 18–20.

Retrieved from http://www.worldfinancialreview.com/?p=483

Hur, S. & Kondo, I. O. (2013). A theory of rollover risk, sudden stops, and foreign reserves.

FRB International Finance Discussion Paper No. 1073. Retrieved from

http://ssrn.com/abstract=2243568 or http://dx.doi.org/10.2139/ssrn.2243568


96

Korinek, A. (2011). The new economics of prudential capital control: A research agenda.

International Monetary Fund Economic Review, 59(3), 523–561.

doi:org/10.1057/imfer.2011.19

Kose, M. A., Prasad, E. S., & Terrones, M. E. (2006). How do trade and financial

integration affect the relationship between growth and volatility? The Institute for

the Study of Labor. Retrieved from


Lane, P. R., & Milesi-Ferretti, G. M. (2006). The external wealth of nations Mark II: Revised

and extended estimates of foreign assets and liabilities, 1970-2004. International

Monetary Fund Working Paper WP/06/69. Retrieved from


Lynch, S. M. (2003). Multicollinearity. Retrieved from

http://www.princeton.edu/~slynch/soc504/multicollinearity.pdf

Managi, S., & Bwalya, S. M. (2010). Foreign direct investment and technology spillovers in

sub-Saharan Africa. Applied Economics Letters, 17(6), 605–608. doi:

10.1080/13504850802167173

Manyena, S. B. (2009). Disaster resilience in development and humanitarian interventions.

(Doctoral dissertation, Northumbria University). Retrieved from

nrl.northumbria.ac.uk/661/1/manyena.siambabala_phd.pdf

Mayer, J. (2006), Learning sequences and structural diversification in developing

countries. Journal of Development Studies, 33(2), 210–229. Retrieved from

http://www.tandfonline.com/doi/abs/10.1080/00220389608422463

Mechler, R., Hochrainer, S., Pflug, G., Lotsch, A., & Williges, K. (2010). Assessing the

financial vulnerability to climate-related natural hazards. World Bank, Policy


97

Research Working Paper 5232, March 2010. Retrieved from

https://openknowledge.worldbank.org/bitstream/handle/10986/3718/WPS5232.txt?

sequence=2

Menson, E. A. (2012). Infrastructure, export-led growth and economic development in sub-

Saharan Africa: An empirical analysis. The IUP Journal of Managerial Economics.

vol. 0(1), pages 46-78. Retrieved from

http://ideas.repec.org/a/icf/icfjme/v10y2012i1p46-78.html

Moody's Investor Service. (May 2013). Moldova's B3 rating reflects low economic

resilience and political instability. Retrieved from

https://www.moodys.com/research/Moodys-Moldovas-B3-rating-reflects-low-

economic-resilience-and-political--PR_273278

Moody’s Standing Committee on Rating Systems & Practices. (June 2009). Moody’s

Rating Symbols & Definitions. Retrieved from

https://www.moodys.com/sites/products/AboutMoodysRatingsAttachments/Moodys

RatingsSymbolsand%20Definitions.pdf

Mougani, G. (2012). An analysis of the impact of financial integration on economic activity

and macroeconomic volatility in Africa within the financial globalization context.

African Development Bank Group. Retrieved from

http://www.afdb.org/fileadmin/uploads/afdb/Documents/Publications/WPS%20No%

20144%20An%20Analysis%20of%20the%20Impact%20of%20Financial%20Integr

ation%20on%20Economic%20Activity%20and%20Macroeconomic%20Volatility%2

0in%20Africa%20%20SS.pdf

Naudé, W., Santos-Paulino, A. & McGillivray, M. (2009). Measuring vulnerability across

outcomes and levels: An overview and Introduction. Oxford Development Studies,

37(3), 183–191. Retrieved from http://website1.wider.unu.edu/lib/files1/ODS-37-3-

09-1-Naude.pdf


98

Pamungkas, B. (2012). Economic resilience of agribusiness households in Putih River

region following the 2010 Merapi volcanism events. (Double masters thesis,

Gadjah Mada University and University of Twente). Retrieved from

http://www.itc.nl/library/papers_2012/msc/aes/pamungkas.pdf

Panitchpakdi, S. (2013, May 9). Developing resilience to financial shocks. Inter Press

Service News Agency. Retrieved from http://www.ipsnews.net/2013/05/developing-

resilience-to-financial-shocks/

Papageorgiou, C. & Spatafora, N. (2012, December 14). Economic diversification in LICs:

Stylized facts and macroeconomic implications. International Monetary Fund Staff

Discussion Note, December 14, 2012, SDN/12/13. Retrieved from

http://www.imf.org/external/pubs/ft/sdn/2012/sdn1213.pdf

Pepe, J. W. (1997). Demonstrating systematic sampling. University of Central Florida.

Retrieved from

http://www2.sas.com/proceedings/sugi22/POSTERS/PAPER213.PDF

Prasad, E. S., Kenneth, R., Wei, S., & Kose, M. A. (2003). Effects of financial globalization

on developing countries: Some empirical evidence. International Monetary Fund.

Retrieved from http://www.imf.org/external/np/res/docs/2003/031703.pdf

Rathbone, J. P. (2013, February 25). Mexico-US trade: the Tijuana logjam. Financial

Times. Retrieved from http://blogs.ft.com/beyond-brics/2013/02/25/mexico-us-

trade-the-tijuana-logjam/?Authorised=false#axzz2jtxe1wWE

Ravallion, M., & Chen, S. (2009, April 30) The impact of the global financial crisis on the

world’s poorest. VOX. Retrieved from http://www.voxeu.org/article/impact-global-

financial-crisis-world-s-poorest


99

Reuttner, I. (2013, January 16). How can financial systems be truly resilience? World

Economic Forum. Retrieved from http://forumblog.org/2013/01/how-can-financial-

systems-be-truly-resilient/

Rose, A. Z. (2009). Economic resilience to disasters. Published Articles & Papers. Paper

75. Retrieved from http://research.create.usc.edu/published_papers/75

Rose, A., & Liao, S. (2005). Modeling regional economic resilience to disasters: A

computable general equilibrium analysis of water service disruptions. Journal of

Regional Science, 45(1), 75–112. doi:10.1111/j.0022-4146.2005.00365.x

Samoilenko, S. & Osei-Bryson, K-M. (2010). Linking investments in telecoms and

productivity growth in the context of transition economies within the framework of

neoclassical growth accounting: Solving endogeneity problem with structural

equation modeling. Proceedings of 18th European Conference on Information

Systems, Pretoria, South Africa, June 6-9, 2010.

Saunders, M. N. K., & Lewis, P. (2012). Doing research in business and management: An

essential guide to planning your project (pp. 1–233). Harlow: Pearson.

Siegel, P. B., Johnson, T. G. & Alwang, J. (1995). Regional economic diversity and

diversification. Growth and Change, 26(2), 261–284. doi:10.1111/j.1468-

2257.1995.tb00171.x

Suttle, P., Koepke, R. & Tiftik, E. (2012, June 7). Capital flows to emerging market

economies. Institute of International Finance. Retrieved from

https://www.google.co.za/search?sourceid=navclient&ie=UTF-

8&q=Suttle%2C+P.%2C+Koepke%2C+R.+%26+Tiftik%2C+E.+(2012).+Capital+Fl

ows+to+Emerging+Market+Economies.+Institute+of+International+Finance+


100

World Bank. (2011). The Multilateral Debt Relief Issue.

http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTDEBTDEPT/0,,content

MDK:20634753~menuPK:64166739~pagePK:64166689~piPK:64166646~theSiteP

K:469043,00.html

UN Secretary General urges integration of Africa into the global economy. (2013, June 2).

Cocorioko Newspaper. Retrieved from http://www.cocorioko.net/?p=48144

Weiers, R. M. (2010). Introductory business statistics. Cengage Learning

Wright, M., Filatotchev, I., Hoskisson, R. E., & Peng, M. W. (2005) Strategy research in

emerging economies: Challenging the conventional wisdom. Journal of

Management Studies, 42(1), 1–33. doi: http://dx.doi.org/10.1111/j.1467-

6486.2005.00487.x

Yanikkaya, H. (2003). Trade openness and economic growth: a cross-country empirical

investigation. Journal of Development Economics, 57–89. Retrieved from

http://www.elsevier.com/locate/econbase


101

Appendix A: Detailed country figures for FDI-to-GDP ratio

Appendix A provides the FDI-to-GDP ratio for each of the SSA countries sampled from the

SSA region as discussed in Section 5.2.

Table A Sample countries FDI-to-GDP ratios

Country name Country code Indicator name FDI-to-GDP (%)

Mauritius MUS Foreign direct investment (% of GDP) 119.0666729

Liberia LBR Foreign direct investment (% of GDP) 84.94215122

Sierra Leone SLE Foreign direct investment (% of GDP) 24.04865279

Guinea GIN Foreign direct investment (% of GDP) 18.76888267

Mozambique MOZ Foreign direct investment (% of GDP) 16.37734931

Sao Tome and Principe STP Foreign direct investment (% of GDP) 13.97687941

Seychelles SYC Foreign direct investment (% of GDP) 12.90846621

Ghana GHA Foreign direct investment (% of GDP) 8.15540319

Namibia NAM Foreign direct investment (% of GDP) 7.584165744

Lesotho LSO Foreign direct investment (% of GDP) 5.608355665

Cape Verde CPV Foreign direct investment (% of GDP) 5.457383953

Sudan SDN Foreign direct investment (% of GDP) 4.772190513

Uganda UGA Foreign direct investment (% of GDP) 4.740949149

Tanzania TZA Foreign direct investment (% of GDP) 4.588229531

Zambia ZMB Foreign direct investment (% of GDP) 4.329366108

Gambia, The GMB Foreign direct investment (% of GDP) 4.007468168

Nigeria NGA Foreign direct investment (% of GDP) 3.289171006

Morocco MAR Foreign direct investment (% of GDP) 2.268329384

Ethiopia ETH Foreign direct investment (% of GDP) 2.071286776

Rwanda RWA Foreign direct investment (% of GDP) 1.666071239

South Africa ZAF Foreign direct investment (% of GDP) 1.480196295


102

Appendix B: Detailed results for Hypothesis 1

Appendix B provides detailed results for Hypothesis 1 referred to in Section 5.4.1.

Table B Hypothesis 1: Results

Country Parameter

Unstandardised coefficients

Standardised coefficients

t Sig.

95.0% Confidence interval for B

Correlations

B Std. error

Beta Lower bound

Upper bound

Zero-order

Partial Part

Liberia internationalreserves -2.99E-

08 0 -0.018

-0.021

0.984 0 0 -0.127 -0.006 -0.006

Sierra Leone internationalreserves -5.77E-

09 0 -0.026

-0.043

0.966 0 0 0.113 -0.012 -0.012

Guinea internationalreserves 2.11E-10 0 0.005 0.008 0.994 0 0 -0.208 0.002 0.002

Namibia internationalreserves 2.12E-08 0 0.857 1.288 0.22 0 0 -0.014 0.336 0.332

Sudan internationalreserves -1.85E-

09 0 -0.078

-0.608

0.555 0 0 -0.008 -0.173 -0.061

Uganda internationalreserves -1.33E-

08 0 -0.752

-5.821

0 0 0 -0.334 -0.85 -0.501

Tanzania internationalreserves 5.94E-09 0 0.65 2.004 0.068 0 0 0.596 0.501 0.236

Zambia internationalreserves 4.15E-09 0 0.155 0.289 0.778 0 0 0.598 0.083 0.061

Nigeria internationalreserves -1.96E-

10 0 -0.17

-0.486

0.636 0 0 -0.01 -0.139 -0.08

Morocco internationalreserves 6.34E-11 0 0.045 0.119 0.907 0 0 -0.118 0.032 0.031

Ethopia internationalreserves -2.67E-

10 0 -0.007

-0.016

0.988 0 0 -0.129 -0.005 -0.004

Rwanda internationalreserves -5.35E-

08 0 -0.35

-1.133

0.278 0 0 -0.544 -0.3 -0.255

South Africa internationalreserves 4.64E-10 0 1.095 1.142 0.274 0 0 0.018 0.302 0.28


103

Appendix C: Detailed results for Hypothesis 2

Appendix C provides detailed results for Hypothesis 2 referred to in Section 5.4.2

Table C Hypothesis 2: Results

Model Parameter Coefficients

Standardised coefficients t Sig.


Correlations

B Std. error Beta Lower Upper Zero- Partial Part

Mauritius concentration_ratio1 22.82 8.7 0.623 2.623 0.021 4.024 41.617 0.261 0.588 0.3

Liberia concentration_ratio1 -77.973 79.99 -0.358 -0.975 0.349 -252.255 96.31 -0.122 -0.271 -0.268

Sierra Leone concentration_ratio1 -12.764 22.316 -0.217 -0.572 0.578 -61.387 35.86 -0.198 -0.163 -0.161

Guinea concentration_ratio1 -0.377 13.639 -0.015 -0.028 0.978 -30.093 29.339 0.268 -0.008 -0.007

Mozambique concentration_ratio1 0.731 4.094 0.032 0.179 0.861 -8.113 9.575 0.234 0.049 0.029

Ghana concentration_ratio1 3.107 9.953 0.066 0.312 0.759 -18.24 24.455 0.365 0.083 0.059

Namibia concentration_ratio1 18.115 20.273 0.303 0.894 0.388 -25.683 61.912 0.05 0.241 0.23

Lesotho concentration_ratio1 1.227 6.877 0.029 0.178 0.861 -13.522 15.976 -0.057 0.048 0.029

Cape Verde concentration_ratio1 6.123 6.138 0.205 0.998 0.335 -7.041 19.287 0.145 0.258 0.204

Sudan concentration_ratio1 -4.851 3.096 -0.403 -1.567 0.143 -11.595 1.894 0.052 -0.412 -0.158

Gambia concentration_ratio1 1.504 6.623 0.066 0.227 0.824 -12.701 15.709 0.02 0.061 0.06

Uganda concentration_ratio1 -2.143 1.399 -0.196 -1.532 0.149 -5.164 0.879 0.113 -0.391 -0.132

Tanzania concentration_ratio1 0.335 10.945 0.006 0.031 0.976 -23.512 24.182 -0.675 0.009 0.004

Zambia concentration_ratio1 -1.451 10.561 -0.048 -0.137 0.893 -24.462 21.559 0.112 -0.04 -0.029

Nigeria concentration_ratio1 7.111 27.344 0.094 0.26 0.799 -52.466 66.688 -0.263 0.075 0.043

Ethopia concentration_ratio1 -13.251 17.135 -0.304 -0.773 0.454 -50.584 24.082 -0.389 -0.218 -0.198

Rwanda concentration_ratio1 11.828 19.845 0.166 0.596 0.561 -31.044 54.7 0.243 0.163 0.134

South Africa concentration_ratio1 -30.118 62.439 -0.268 -0.482 0.638 -165.01 104.775 0.227 -0.133 -0.118


104

Appendix D: Detailed results for Hypothesis 3

Appendix D provides detailed results for Hypothesis 3 referred to in Section 5.4.3.

Table D Hypothesis 3: Results

Country Parameter

Unstandardised Standardise

d t Sig.


Correlations

B Std. error

Beta Lower Upper Zero Partial Part

Mauritius openness -3.40E-10 0 -0.246 -1.244 0.236 0 0 -0.74 -0.326 -0.142

Liberia openness -1.64E-07 0 -0.363 -0.389 0.704 0 0 -0.147 -0.112 -0.107

Sierra Leone openness 2.70E-09 0 0.04 0.057 0.955 0 0 0.148 0.016 0.016

Guinea openness -7.75E-10 0 -0.235 -0.282 0.783 0 0 -0.381 -0.081 -0.075

Mozambique openness -2.31E-09 0 -0.459 -2.326 0.037 0 0 -0.71 -0.542 -0.376

Namibia openness -2.91E-09 0 -0.846 -1.412 0.181 0 0 -0.156 -0.365 -0.364

Cape Verde openness -1.01E-08 0 -0.632 -3.083 0.008 0 0 -0.613 -0.636 -0.629

Gambia openness -2.73E-09 0 -0.114 -0.394 0.699 0 0 -0.088 -0.105 -0.105

Sudan openness 1.03E-09 0 0.69 2.525 0.027 0 0 -0.196 0.589 0.255

Tanzania openness -1.19E-09 0 -0.557 -2.09 0.059 0 0 0.274 -0.517 -0.247

Zambia openness 2.44E-09 0 0.555 1.132 0.28 0 0 0.664 0.311 0.238

Nigeria openness 9.80E-11 0 0.237 0.428 0.676 0 0 -0.242 0.123 0.071

Ethiopia openness 6.82E-10 0 0.083 0.206 0.84 0 0 0.099 0.059 0.053


105

Appendix E: Detailed Results for Hypothesis 4

Appendix E provides detailed results for Hypothesis 4 referred to section 5.4.4.

Table E Hypothesis 4: Results

Country Parameter

Unstandardised coefficients

Standardised coefficients t Sig.


Correlations

B Std. Error Beta Lower Upper Zero Partial Part

Mauritius integration -9.42E-09 0 -0.873 -4.367 0.001 0 0 -0.557 -0.771 -0.499

Liberia integration 2.79E-08 0 0.132 0.268 0.794 0 0 -0.045 0.077 0.074

Sierra Leone integration 3.20E-09 0 0.088 0.222 0.828 0 0 0.036 0.064 0.062

Mozambique integration -8.53E-10 0 -0.454 -2.124 0.053 0 0 -0.719 -0.508 -0.343

Ghana integration -4.01E-09 0 -0.669 -3.142 0.007 0 0 -0.699 -0.643 -0.599

Lesotho integration -3.59E-09 0 -0.792 -4.797 0 0 0 -0.789 -0.788 -0.787

Cape Verde integration -4.68E-08 0 -0.882 -1.469 0.168 0 0 -0.692 -0.39 -0.282

Sudan integration -2.41E-09 0 -1.158 -8.064 0 0 0 -0.892 -0.919 -0.815

Uganda integration -7.14E-09 0 -0.912 -10.073 0 0 0 -0.74 -0.942 -0.868

Tanzania integration -2.16E-09 0 -0.687 -3.409 0.005 0 0 -0.877 -0.701 -0.402

Zambia integration 6.04E-10 0 0.144 0.474 0.644 0 0 0.096 0.135 0.1

Nigeria integration -4.11E-10 0 -0.923 -2.842 0.015 0 0 -0.816 -0.634 -0.469

Morocco integration -9.65E-11 0 -0.225 -0.594 0.562 0 0 -0.193 -0.157 -0.156

Ethiopia integration -1.25E-09 0 -0.29 -0.552 0.591 0 0 -0.3 -0.157 -0.141

Rwanda integration -8.56E-09 0 -0.274 -0.941 0.364 0 0 -0.405 -0.252 -0.212

South Africa integration -1.69E-10 0 -1.085 -1.497 0.158 0 0 -0.241 -0.383 -0.367


106

Appendix F: Detailed Results of Multi- Collinearity

Appendix F provides detailed analysis for multicollinearity analysis as discussed in Section

5.5.

Table F1 Mauritius: Pearson correlation

GDP

change Concentration

ratio International

reserves Trade

openness Economic integration

Pearson correlation

GDP change 1 0.261 -0.717 -0.74 -0.557

Concentration ratio

0.261 1 -0.624 -0.559 0.573


-0.717 -0.624 1 0.952 0.112

Trade openness

-0.74 -0.559 0.952 1 0.167

Economic integration

-0.557 0.573 0.112 0.167 1

Table F2 Mozambique: Pearson correlation

GDP

change Concentration

ratio International

reserves Trade

openness

Economic integratio

n

Pearson correlation

GDP change 1 0.234 -0.666 -0.71 -0.719

Concentration ratio

0.234 1 -0.138 -0.057 -0.388


-0.666 -0.138 1 0.962 0.619

Trade openness

-0.71 -0.057 0.962 1 0.549


-0.719 -0.388 0.619 0.549 1

Table F3 Ghana: Pearson correlation

GDP

change Concentratio

n ratio International

reserves Trade


Pearson correlation

GDP change 1 0.365 0.288 0.285 -0.699

Concentration ratio

0.365 1 0.79 0.661 -0.446


0.288 0.79 1 0.902 -0.284

Trade openness

0.285 0.661 0.902 1 -0.357


-0.699 -0.446 -0.284 -0.357 1


107

Table F4 Lesotho: Pearson correlation

GDP

change Concentratio


reserves Trade


Pearson correlation

GDP change

1 -0.057 -0.819 -0.793 -0.789

Concentration ratio

-0.057 1 0.075 0.008 0.11


-0.819 0.075 1 0.934 0.902

Trade openness

-0.793 0.008 0.934 1 0.954


-0.789 0.11 0.902 0.954 1

Table F5 Cape Verde: Pearson correlation

GDP

change Concentratio


reserves Trade


Pearson correlation

GDP change

1 0.145 -0.529 -0.613 -0.692

Concentration ratio

0.145 1 0.076 0.095 0.105


-0.529 0.076 1 0.965 0.877

Trade openness

-0.613 0.095 0.965 1 0.94


-0.692 0.105 0.877 0.94 1

Table F6 Namibia: Pearson correlation

GDP

change Concentratio


reserves Trade


Pearson correlation

GDP change

1 0.05 -0.014 -0.156

Concentration ratio

0.05 1 -0.459 -0.165


-0.014 -0.459 1 0.864

Trade openness

-0.156 -0.165 0.864 1


1


108

Table F7 Gambia: Pearson correlation

GDP

change Concentratio


reserves Trade


Pearson

correlatio

n

GDP change

1 0.02 -0.056 -0.088 -0.069

Concentration ratio

0.02 1 0.387 0.397 0.302


-0.056 0.387 1 0.992 0.965

Trade openness

-0.088 0.397 0.992 1 0.975


-0.069 0.302 0.965 0.975 1

Table F8 Nigeria: Pearson correlation

GDP

change Concentratio


reserves Trade


Pearson correlation

GDP change

1 -0.263 -0.01 -0.242 -0.816

Concentration ratio

-0.263 1 -0.433 -0.633 0.304


-0.01 -0.433 1 0.763 -0.022

Trade openness

-0.242 -0.633 0.763 1 0.314


-0.816 0.304 -0.022 0.314 1

Table F9 Morocco: Pearson correlation

GDP

change Concentratio


reserves Trade


Pearson correlation

GDP change

1 -0.118 -0.15 -0.193

Concentration ratio

1


-0.118 1 0.948 0.723

Trade openness

-0.15 0.948 1 0.854


-0.193 0.723 0.854 1


109

Table F10: Rwanda: Pearson correlation

GDP

change Concentratio


reserves Trade


Pearson correlation

GDP change

1 0.243 -0.544 -0.528 -0.405

Concentration ratio

0.243 1 -0.385 -0.399 0.211


-0.544 -0.385 1 0.993 0.473

Trade openness

-0.528 -0.399 0.993 1 0.457


-0.405 0.211 0.473 0.457 1

Table F11 South Africa: Pearson correlation

GDP

change Concentratio


reserves Trade


Pearson

correlatio

n

GDP change

1 0.227 0.018 -0.196 -0.241

Concentration ratio

0.227 1 0.677 0.256 0.227


0.018 0.677 1 0.783 0.826

Trade openness

-0.196 0.256 0.783 1 0.961


-0.241 0.227 0.826 0.961 1


110

Appendix G: Detailed Results of Standardised Coefficients

Appendix G refers to detailed analysis for standardized coefficient analysis, as discussed in

chapter 6.

Table G Standardised coefficients

concentration_ratio international_reserves openness integration

Cape Verde 0.228 0.559 -0.345 -0.882

Ethiopia -0.304 -0.007 0.083 -0.29

Gambia 0.151 2.061 -2.861 0.686

Ghana -0.104 0.424 -0.289 -0.728

Guinea -0.015 0.005 -0.235 -0.199

Lesotho 0.022 -0.602 0.066 -0.31

Liberia -0.358 -0.018 -0.363 0.132

Mauritius 0.637 0.053 -0.287 -0.879

Morocco . 0.06 -0.021 -0.218

Mozambique 0.077 1.002 -1.361 -0.562

Namibia 0.303 0.857 -0.846 .

Nigeria 0.094 -0.17 0.237 -0.923

Rwanda 0.18 -1.197 0.854 -0.268

Sierra Leone -0.217 -0.026 0.04 0.088

South Africa -0.532 1.534 0.953 -2.303

Sudan . -0.135 0.319 -1.024

Tanzania 0.006 0.65 -0.557 -0.687

Uganda -0.201 -0.774 0.02 -0.91

Zambia -0.048 0.155 0.555 0.144

Avg Mean -0.005 0.233 -0.213 -0.507
